dataesr/scientific-paragraphs-categorization huggingface dataset:
EFEMERIS (Évaluation chez la Femme Enceinte des MÉdicaments et de leurs RISques) is another database intended specifically to evaluate drug safety in pregnancy  [44]. At the present time, this French database includes 128,053 mother-child pairs from 1 July 2004 until 31 December 2016 in South West France  [45]. Data come from four different sources (Fig. 1 ). First, the French health insurance system records all reimbursed drugs (name, ATC code, date of dispensation, prescriber) prescribed and dispensed to patients under general state coverage. Secondly, the mother and child protection centre (PMI) records data from children certificates filled out during the compulsory medical examinations at birth, 9 and 24 months (according to the French law). These health certificates contain data about the health of the mother (maternal characteristics, some pathologies during pregnancy) and the child (weight, size, Apgar score, neonatal pathologies, psychomotor development, congenital malformations).
Data sources for studying long-term effects after prenatal drug exposure Finally, some healthcare databases are augmented with clinical data collected during the infant follow-up, enabling the study of outcomes that can only be diagnosed later in childhood. For example, the cohort POMME (PrescriptiOn Médicaments Mères Enfants/Mothers and children drug prescriptions), has been implemented in 2011 to evaluate long-term effects of prenatal exposure to drugs (Fig. 2 )  [46]. Children born in Haute-Garonne (South-Western France), over a period of one year (from the 1st of July to the 30th of June), are registered in POMME every 5 years. The cohort began the 1st of July 2010. To date, 8,372 children are recorded in POMME.
POMME provides anonymous medical data about mothers and children. Data sources included i) French health insurance database (drugs and medical care prescriptions and reimbursements to children and mothers during pregnancy) and ii) mother and child protection centre database (child health certificates at birth, 9 and 24 months). POMME yearly collects, from the French health insurance system, anonymous data about prescribed and reimbursed drugs and medical care to children. Thus, POMME holds information about drugs reimbursed to children during prenatal life (from conception) and infancy  [47]. Two studies have already been conducted using the POMME cohort. The first aimed to evaluate the association between in utero exposure to drugs that potentially exhibit immunosuppressive activity and occurrence of infections in childhood  [47].
In Sweden, was implemented the Stockholm youth cohort, an intergenerational record linkage study comprising all individuals aged 0-17 living in Stockholm county in 2001-2011. It contains prospectively recorded data on the cohort members and their parents collected by record linkage with a range of national and regional healthcare, social, and administrative registries. This cohort has allowed, for example, to carry out a study assessing the risk of autism in offspring after prenatal exposure to antidepressant  [51]. Another cohort was implemented in Denmark, in 1973, to monitor the health of pregnant women and their offspring: the Danish medical birth register  [52]. The Danish unique civil registration number enables the linkage to several Danish health registers, including the Danish national prescription registry, which contains individual information on redeemed prescriptions from all Danish outpatient pharmacies.
This may limit the recall bias concerning drug exposure during pregnancy. Many studies have been conducted using the Danish medical birth register, for example to investigate whether antiepileptic or antipsychotic medications exposure in fetal life are associated with use of primary healthcare in childhood  [53, 54]. Finally, the IADB. nl-database also provides information to study long-term consequences of prenatal exposure. It contains pharmacy prescription data of an estimated population of 500,000 individuals from the Netherlands. The infant can be linked to his/her mother using an address code (approximately 65% of the infants). Despite the limited clinical data since it is a pharmacy prescription database, studies have been performed using this database, assessing the association between prenatal exposure and the dispensing of medications in childhood as a proxy for pathologies  [55, 56].
Strengths and limitations of the use of healthcare databases for pharmacoepidemiologic studies in pregnancy The main weaknesses of healthcare databases are the lack of information on over-the-counter exposures and whether the woman actually took the drug. There are also concerns about the quality of data recorded. Indeed, missing data, misclassification of exposure and inaccurate information on pregnancy outcome are likely to impair the medication evaluation. For example, pregnancy terminations are sometimes missing  [57]  and only live births are described. This is an important issue, since some pregnancies might have been terminated due to drug-induced malformation in the fetus. If the information is missing, the teratogenic risk could be underestimated. Only considering live births could cover up drug-induced pregnancy losses.
Moreover, claims databases often have missing information on potentially important confounders as for example comorbidities, drugs dispensed during hospitalization, use of alcohol, smoking, socio-economic level and body-massindex. Some studies are therefore prone to bias resulting from confounding factors, even if these biases often can be considered in statistical analysis. Dealing with complex confounding problems presents new challenges. Nevertheless, healthcare databases are valuable tools to assess the drug safety during pregnancy. Firstly, they provide the advantage that they use real-life data. Unlike clinical trials, patients captured by databases are rather representative of the general population (multiple illnesses, polypharmacy). Another potential strength is that they provide large size sample, enabling pharmacoepidemiologic studies to detect rare effects provided that they are conducted using sources with adequate sample size.
For example, to detect a doubled risk of malformation after prenatal exposure to drugs, the minimum number of exposed subjects who need to be enrolled in the study is higher than 1000. To detect an increase in the risk of a specific malformation which prevalence in the general population is 0. 1% (such as oral cleft), 20 times more patients are required: in a database of 100 000 pregnant women, at least one out of five must have taken the medication. A review of European health care databases in 2014 showed that the number of pregnancies in these databases ranges from 27 000 to 130 000 according to the country  [57]. The large amount of data available allows studying several outcomes or several medications. The method for recording patient and for collecting data presents the advantage to limit both selection and recall bias.
Thus, when focusing on a specific congenital abnormality and infrequently used medications, the study should be conducted using a so large-sized sample that a single database might be not sufficient. To that end, consortiums have been developed in recent years enabling multicentric studies. For example, EUROmediCAT is a European research consortium dedicated to improving medication safety in pregnancy  [58]. It started work on 1 March 2011 with financial support provided by the European Union and was established as a daughter project to European Surveillance of Congenital Anomalies (EUROCAT). Since then, EUROmediCAT has diversified its funding sources, as can be seen under EUROmediSAFE, which is an independent research consortium comprising of 11 partner institutions from Belgium, France, Germany, Italy, the Netherlands and the UK.
Drug utilization study Several epidemiologic study designs can be used to assess medication safety during pregnancy. The first one is the drug utilization study. Such a study has been performed using the EFEMERIS database for example. It focused on prescriptions for asthma medications before and during pregnancy  [60]. A change in the type of asthma medication during pregnancy was highlighted with an increase in the prescribing of better evaluated medications. This observation has guided the following research to study the potential impact of such changes for fetuses and newborns and to give better information for health professionals. Drug utilization studies are important because they provide the denominator data needed in pharmacovigilance. They also give information about the drug utilization in real life  [61, 62].
However, such studies are descriptive and do not allow for the identification of association between drug exposure and pregnancy outcome. As many drugs are not approved in obstetrics, they are often used off-label and it becomes important to know the drug exposure among pregnant women. For example, a study using the EFEMERIS database was carried out to describe calcium channel blockers prescription patterns during pregnancy, used as a maintenance therapy to prevent preterm labour  [63]. Although they have not been demonstrated to be effective such a use, a wide ambulatory prescription of CCB to pregnant women (between 2 and 3 % of the pregnant women) on late pregnancy was observed.
Case/non-case study Secondly, comes risk assessment study. This includes case/non-case study, which can be performed using Pharmacovigilance databases. This approach measures disproportionality of combination between a drug and a particular adverse pregnancy outcome. A study conducted in VigiBase®, the WHO global ICSR database, evaluated signals of congenital malformations with antipsychotics  [64]. The authors found a signal of disproportionate reporting for antipsychotics and gastrointestinal congenital abnormalities, which requires further studies using different designs to be confirmed.
Case-control study Another type of risk assessment study is case-control study. In this design, the exposure rate among cases (individuals with a specific outcome, for example a congenital malformation) is compared with the exposure rate among controls. The main advantage of this approach is the high statistical power when studying specific malformations, provided that the sample size and prevalence of exposure are adequate. This requires important consideration of the potential confounders. However, case-control studies that collect information from parental surveys or interviews are prone to recall bias that may often limit the results interpretation. The same does not apply for casecontrol studies conducted using electronic databases. Registries are good tools to perform case-control studies.
The authors concluded that vaccination with a non-adjuvanted A/H1N1 vaccine during pregnancy was not significantly associated with an increased risk of adverse pregnancy outcomes. Cohort study designs are also appropriate to assess long-term effect of in utero drug exposure. A study conducted in EFEMERIS has evaluated the effect of prenatal exposure to drugs with atropinic properties on infant psychological development  [67]. Data for psychological development were extracted from children's medical certificates completed at 9 and 24 months. This study showed a significant association between in utero exposure to atropinic drugs and fewer infant cognitive acquisitions at 24 months.
Different data analysis methods Non classical drug utilization studies offer an alternative to estimate in utero drug exposure. For example, a recent study has used the trajectory method to identify profiles of exposure (in terms of intensity and evolution during pregnancy) and evaluate the effects of in utero exposure to anxiolytics and hypnotics on the adaptation to extra uterine life  [68, 69]. Pregnant women were classified according to their profile exposure, which was determined in three steps: (a) Conversion of prescription data into exposure variables (using ATC-DDD), (b) Construction of individual trajectories of exposure and (c) Clustering of individual trajectories of exposure. The study of Hurault-Delarue et al  [69]  presents the four clusters of pregnant women with homogeneous trajectories of exposure that were identified.
Patient Selection Data of all consecutive patients who underwent TSH for bilobar CRLM were retrieved from prospective databases at the following centers: 1) Hôpital Pitie Salpêtriere (2014-2017), 2) Hôpital Saint-Antoine (2009-2014) [1) and 2) were performed by the same surgical team], nd 3) Institut Mutualiste Montsouris (2007-2017). Only the patients who completed the first stage (left lobe clearance with partial hepatectomies) and the second stage (standard/extended right hepatectomy) were included to allow appropriate comparison of operative characteristics. According to the type of operative approach, patients were categorized into two groups; laparoscopic TSH (both stages were performed by laparoscopic approaches) and open TSH (both stages were performed by open approaches). Patients who underwent differing approaches between the first and second stage were excluded from the analysis.
Statistical analysis Data are presented as medians with ranges for continuous variables. Continuous variables were analyzed with a non-parametric approach using the Mann-Whitney U test. Categorical variables were compared using the chi-square test or Fisher's exact test as appropriate. For propensity score matching (PSM), the logistic regression model was based on the assessment of goodness-of-fit statistics  32. In this setting, propensity score adjustment was performed on the factors including baseline patient characteristics [age, sex, American Society of Anesthesiologists physical status score (ASA score)], primary cancer locations (colon/rectum), preoperative chemotherapy, maximal size and number of lesions, concomitant colorectal resection, and PVO. Using these propensity scores, patients treated with laparoscopic TSH and those treated with open TSH were matched randomly 1:1 with a caliper width of 0.
20 of the standard deviation of the score. After assuring the comparability of the groups, postoperative outcomes were compared between the groups. Rates of OS, RFS, and liver-RFS were calculated from the date of the second stage hepatectomy. Cumulative OS, RFS, and liver-RFS rates were calculated using the Kaplan-Meier method with differences between the curves evaluated using the log-rank test. P <. 05 values were considered statistically significant. All statistical analyses were performed using JMP 11 (SAS Institute, Cary, NC) and Prism 6 (GraphPad Software, Inc. , La Jolla, CA).
1c-e ), possibly imprinted by the graft-versus-leukemia (GvL) effect. To overcome the redundancy of gene ontology categorization, we assessed the semantic distance between the significantly deregulated biological processes using the GOSemSim Bioconductor R package  10  and identified two deregulated 'macro-clusters' encompassing genes linked to T cell costimulation and to antigen processing and presentation via HLA class II molecules (Fig. 1f ). This result was confirmed by an independent bioinformatic analysis with the ClueGO package from Cytoscape  11  (Extended Data Fig. 2 ). Given the deregulation of the T cell costimulation process highlighted in the previous analyses, we retrieved from the original dataset the expression levels of 32 genes known to be relevant in conveying activating or inhibitory signals to T cells and used them to create a heatmap representing the fold change in the expression of these genes between leukemia at diagnosis and its counterparts at relapse after chemotherapy or allo-HCT (Fig.
NATuRE MEDICINE that at least in some patients with deregulation of the PD-1/PD-L1 axis, checkpoint blockade might restore a proficient GvL effect against the relapsed disease. We next extracted from our dataset the expression of transcripts involved in peptide proteolysis and HLA presentation, selected from the Reactome database  12. This analysis revealed the frequent and significant downregulation at post-transplantation relapse of almost all HLA class II transcripts and of their known regulator CIITA (Fig. 3a ), which was confirmed by locus-specific quantitative PCR (Fig. 3b ) and not explained by genomic alterations detectable by SNP arrays (Extended Data Fig. 1  and Supplementary Table  3 ). Comparative immunophenotypic analysis of leukemia blasts collected before and after allo-HCT confirmed loss of HLA-DR and -DP cell surface expression in 7/33 relapses (Fig.
4a ), as were the patterns and relative frequency of the transcriptional changes documented in genes involved in T cell costimulation and HLA class II presentation (Fig. 4b  and Extended Data Fig. 7 ). To analyze the reciprocal interactions of the two newly identified modalities of relapse, the immunophenotypic datasets originated from the two cohorts were clustered on the basis of profile similarity (Fig. 4c, d ). This allowed to clearly distinguish in both cohorts a subset of patients with downregulation of HLA class II molecules (top clusters, 45% of cases in the discovery set and 39% in the validation set), and a second macro-cluster characterized by minor changes (middle sub-clusters) or upregulation (lower sub-clusters) of HLA class II molecules, accompanied by more evident upregulation of inhibitory markers, including PD-L1.
Study design: patient and transplant characteristics. In this retrospective study, on the basis of material availability, we selected a discovery cohort (n = 40) and a validation cohort (n = 36) of patients with a diagnosis of de novo or secondary AML who experienced non-HLA loss disease relapse after allo-HCT, and for whom paired pre-and post-transplant viable leukemic samples had been collected on specific written informed consent, in agreement with the Declaration of Helsinki. Discovery cohort samples came from the San Raffaele Leukemia Biobank (Protocol ' ALLO-RELAPSE' approved by the San Raffaele Ethics Committee on 3 November 2017 and Protocol 'Banca Neoplasie Ematologiche' approved by the San Raffaele Ethics Committee on 5 October 2010, latest amendment on 14 June 2012) and the Northern Italy Leukemia Group (NILG) BioBank (protocol NILG AML 02/06, NCT00495287).
Nucleic acid quantification was performed using a Nanodrop-2000c spectrophotometer (Thermo Fisher Scientific), and quality control was performed taking advantage of the Agilent Bioanalyzer technology or the RNA Screen Tape System (both from Agilent). SNP array and data analysis. Genomic profiling was performed using the Illumina Human 660W-Quad BeadChip array (Illumina), capable of assessing over 550,000 tag SNPs plus 100,000 further markers (targeting regions of common copy number variations). A total of 200 ng per sample of high-quality DNA were amplified, tagged and hybridized according to the manufacturer's protocol. The array slides were scanned on an iScan Reader (Illumina). The estimations of log 2 Ratio (log 2 R) and B-allele frequency (BAF) were generated using default settings of the GT module of GenomeStudio v.
9. 4 (Illumina), taking into consideration the HapMap control set provided by the manufacturer. On the basis of the log 2 R and BAF values, Nexus Copy Number 5. 0 (BioDiscovery) with default settings was used for calling copy number variation and/or LOH events, with the UCSC built Hg18-Human Mar. 2006 (NCBI36/hg18) assembly as reference. Raw data tables from GenomeStudio were then imported and processed by the DNAcopy R package for segmentation analysis estimation  36. The log 2 R, BAF and the segmentation data were converted into a BED graph and segment files, and alterations previously identified were confirmed by visual inspection on IGV v. 2. 4. 8 (ref. 37  ). Supplementary Table  3  lists all duplications, deletions and CN-LOHs larger than 0. 1 Mb detected by our analysis, together with estimates of clonality of the alterations, through a method adapted from Paulsson et al.
Gene expression profile array and data analysis. Gene expression profiling for samples belonging to the discovery set was performed using the Illumina HumanHT-12 v. 3. 0 or v. 4. 0 Expression BeadChips array (Illumina), covering up to 35,000 annotated genes with more than 48,000 probes derived from the National Center for Biotechnology Information Reference Sequence (NCBI) RefSeq (Build v. 36. 2, Release 22) and the UniGene (Build 1999) databases. A first step of RNA amplification generating biotinylated, amplified RNA for hybridization was performed with the Illumina TotalPrep RNA Amplification Kit (Life Technologies), according to previously published protocols and to the manufacturer's recommendations  39. Briefly, up to 200 ng of total RNA was reverse transcribed into complementary DNA with T7 Oligo(dT) primers, and the double stranded cDNA was then in vitro transcribed to synthesize cRNA using a biotin-NTP mix.
The resulting cRNA was quantified on a Nanodrop-2000c spectrophotometer (Thermo Fisher Scientific), and the quality was assessed on a Agilent Bioanalyzer chip (Agilent). A total of 250 ng of cRNA were then hybridized to the BeadChip at 58 °C overnight and the fluorescent signal was developed with streptavidin-Cy3, followed by quantitative detection of fluorescence emission by the array Illumina iScan scanner and computation by the Illumina GenomeStudio software (both from Illumina). Gene expression data were normalized using the quantile algorithm implemented in the Illumina GenomeStudio software. Among the 30,000 genes assessed in the microarray, a matrix of expressed genes was generated by selecting all transcripts with intensity values that differed significantly from background (detection P < 0.
01) in at least one sample of the entire series. The LIMMA Bioconductor package was used to extract the DEGs considering a factorial design model and pairwise comparisons  40. A post-test was used to select putative DEGs in each contrast, under the Benjamini-Hochberg multiple comparison correction. Genes with an adjusted P< 0. 1 were considered differentially expressed. A gene ontology enrichment analysis for biological processes was performed with the DAVID v. 6. 7 online interface  41, 42  , using default parameters and considering as background all the genes expressed in our dataset and as genes of interest those differentially expressed in each specific contrast. For each comparison, P < 0. 05 was used to define a significantly enriched biological process. The GOSemSim package release 2.
12 (ref. 10  ) and the ClueGO package from Cytoscape 11 were used to cluster together related gene ontology biological processes. RNA-seq and data analysis. Gene expression profiling for samples belonging to the validation set was performed using RNA-seq, starting from 300 ng of total RNA and using the TruSeq Stranded mRNA library preparation kit (Illumina) in accordance with low-throughput protocol. After PCR enrichment (15 cycles) and purification of adapter-ligated fragments, the concentration and length of DNA fragments were measured using D1000 Screen Tape System (Agilent), obtaining a median insert size of 311 nucleotides. Then, RNA-seq libraries were sequenced using the Illumina Next-Seq 500 high platform to obtain a minimum of 30 × 10 6 paired-end reads per sample. To quantify gene expression levels, read tags were pseudoaligned to gencode v.
28 transcripts 43 using kallisto v. 0. 44. 0  44  (parameters: -t 8-single-rf-stranded -l 200 -s 20). Abundancies were summarized to genes using txImport package  45  and analyzed using edgeR  46  with a design matrix including sample identity, disease condition and coordinates of the first two components of multidimensional scaling of the count matrix (~sample_id + condition + X + Y). Fold change was then calculated on the 'condition' covariate. Quantitative PCR quantification of gene transcripts. qPCR assays for the quantification of HLA-A, HLA-C, HLA-DRB, HLA-DPB1 and CIITA transcripts were developed in house or adapted from previous studies  [47] [48] [49]. For all reactions, 250-500 ng of total RNA were retro-transcribed with the High Capacity cDNA Reverse Transcription Kit (Applied Biosystems), using random hexamers and RNase Inhibitor.