dataesr/scientific-paragraphs-categorization huggingface dataset:
Study design The study was approved by the local ethic committee before the study commencement considering that no patients data were collected, and samples were anonymised. We conducted a prospective multicenter study including 23 sampling sites belonging to 6 medical biology laboratories in the Bourgogne-Franche-Comteregion (France). Laboratories sent to a central laboratory (in the University Hospital of Besancon) all isolates identified as ESBL-Ec or MRSA collected in the first three months of 2019 and 2021 from clinical samples of community patients (only one isolate per patient). Isolates were then categorized according to their origin (urban area >100,000 inhabitants, semi-urban area between 20,000 and 100,000 inhabitants and rural area if < 20,000 inhabitants). The authors did not have access to any information that could identify individual participants during or after data collection.
Genome sequencing and assembly, antibiotic and metal resistance, virulence factors and typing Bacterial DNA was extracted using QIAGEN DNeasy UltraClean Microbial Kit (Qiagen, Hilden, Germany) according to the manufacturer's recommendations. The extracted DNA was sequenced on an Illumina NextSeq (150-bp paired-ends reads and a coverage of > 80X). The read sequences are available in the NCBI BioProject PRJNA975705. Raw reads were trimmed using Trimmomatic v0. 39  [17]  and assembled with SPAdes v3. 13. 0  [18]. Resistance genes were identified using ResFinder v4. 0  [19]  by KMA method  [20]  for enzymatic resistances and using AMRFinderPlus v3. 1  [21]  to identify point mutations for antibiotic resistance. Virulence genes were identified using VirulenceFinder v2. 0  [22, 23]  and VFDB  [24].
Multi-locus sequence typing (MLST) was performed with pyMLST v2. 1. 2 (https://github. com/bvalot/pyMLST) (using Achtman and Enright schemes, for E. coli and S. aureus, respectively). For ESBL-Ec, the virulence genes were classified according to their function as adhesin, toxin and iron acquisition; phylogroup was determined using ClermonTyping v3  [25] ; the search for plasmid, chromosomal, or unclassifiable location of ESBL-encoding genes was performed using PlaScope v1. 3. 1  [26]. For MRSA, metal resistance genes were searched using AMRFinderPlus v3. 1; the spa and SCCmec cassette typing were performed using spaTyping  [27]  and SCCmecFinder  [28] , respectively. All analyses performed had a minimum identity percentage of 90% and a minimum coverage length of 80%.
Phylogenetic trees-Core genome MLST (cgMLST) Core genomes of ESBL-Ec and MRSA isolates were determined using pyMLST by aligning genes present from existing schemes deposited on the cgMLST nomenclature server (https:// www. cgmlst. org/ncs). Two maximum-likelihood tree were constructed with a GTR + CAT module using FastTree 2. 1  [29]. Trees were rooted with the genomes of E. fergusonii (GCF_013892435. 1) and S. schweitzeri (GCF_900636685. 1) for ESBL-Ec and MRSA, respectively. Trees were visualized and annotated with iTOL v6. 6  [30].
Statistics analyses Descriptive and analytical statistics were performed using the GraphPad Prism software, version 9. 3. 0 (California, USA). The Shapiro-Wilk test was applied to assess data normality. Pearson's correlation test was performed for parametric data, and Spearman's correlation test for non-parametric data. For analyzing NAb titers in both serum and saliva samples, the T-test was used for parametric data and the Mann-Whitney test for non-parametric data to compare the negative control to vaccinated participants. To compare the three time points of the vaccinated group to the control group Kruskal-Wallis test was used followed by Dunn's multiple comparisons post-test. In addition, to compare the values for the three different time points with each other for vaccinated group, the Friedman test for matched samples was used followed by Dunn's multiple comparisons post-test.
Ethics approval The study was performed in accordance with the Declaration of Helsinki. Ethical approval for this study (Ethics Committee N° ID-RCB: 2019-A01949-48) was obtained from the Comité de Protection des Personnes Ile de France X, France, on November 14, 2019 (Pr P. Casassus). In accordance with French law, all patients were provided with written information about the study and their informed consent to participate was obtained. This study has been registered on Clinicaltrials. gov: NCT04223414. This manuscript complies with the applicable guidelines of STROBE.
Study design Fluid challenge was performed at the discretion of the physician according to standard recommendations  [4, 5]  and consisted of infusion of 250 ml of crystalloid (0. 9% saline) over 10 min. Data were collected immediately before and 4-5 min after fluid infusion. Bolus administration or changed in vasopressor dosage, as well as changes in ventilatory parameters or anesthesia drugs dosage, were not allowed during fluid infusion. Multiple fluid challenges could be performed in the same patient depending on whether SV had been previously increased by more than 10% or at the discretion of the physician according to recommendations.
Data collected before and after volume expansion were compared with a paired Student t test or a paired Mann-Whitney test, depending on their distribution. The relationship between ANIm and SV variation was assessed by using a linear correlation. Diagnostic performance of change in ANI to diagnose fluid responsiveness was estimated using Receiver operating characteristic curves, the AUROC, and their confidence interval (CI). Because of the repeated fluid challenges, CI were estimated by using an individual Boostrap method with 1000 replication. The best cutoff value was by maximizing the Younden index (sensitivity + specificity -1). We tested the diagnostic performance of changes in ANI during volume expansion according to remifentanil dose regimen and baseline ANI value using the Faraggi's method  [22].
Real data results Finally, we decided to evaluate our strategy with real clinical data, although the proposed method is only a first step in which we linearized the original blood flow model. The data, an in vivo measurement of aortic pressure, were recorded and anonymized by F. Vallée (M. D. and D. r), anesthetist at Lariboisière Hospital in Paris (APHP). These data are measured with an intra-aortic catheter equipped with a pressure transducer capable of recording blood pressure and velocity over time. The pressure measured directly over the renal artery is used as an observation and the blood flow coming from the left ventricle is reconstructed. The configuration of the IP procedure is shown in Figure  5 , and the data injected into the model and the estimated source are shown in the boxes on the right and left sides of the vessel, respectively.
7, 9  Even less evidence is available on long-term outcomes and their determinants. 4, 10, 11  Although several studies investigated in-hospital outcomes, data on survival of patients who underwent postcardiotomy ECMO after discharge are lacking and urgently needed. 10, 11  Besides the evidence-based support for the patient selection process, the intraoperative and postoperative optimization of ECMO management are required to address patient's needs and guide ECMO application. This may guarantee a more effective personalized and timely therapy, optimize use of resources, and improve in-hospital and postdischarge outcomes. The PELS-1 (Postcardiotomy Extracorporeal Life Support) study includes data on adults experiencing postcardiotomy cardiogenic shock and requiring ECMO in an international group of participating hospitals.
Data Collection and Outcomes Demographics, preoperative clinical and laboratory variables, procedural characteristics, ECMO treatment modality, cannulation strategy, in-hospital morbidity and mortality, as well as postdischarge survival were collected from each participating hospital and included in a dedicated electronic case report form (data. casto redc. com), according to the predefined protocol and variable definitions (Data S1 and Table  S2 ). The full data set was retained and centrally managed by the coordinating center, which had full access to all the data in the study and takes responsibility for their integrity and the data analysis. Long-term follow-up data were collected through the review of the most recent medical records or contact with patients at discretion of the treating center.
2, 10, 11, 13, 14  For the association with in-hospital mortality, we used the following: (1) demographic data and preoperative variables; (2) demographic data and preoperative and intraoperative variables; (3) demographic data and preoperative, intraoperative, and ECMO variables; or (4) demographic data, preoperative, intraoperative, and ECMO variables, and postoperative complications. Finally, a subgroup survival analysis was performed including hospital survivors only. A multivariable model to identify variables associated with postdischarge mortality was performed using the mixed Cox proportional hazards model in the subgroup of in-hospital survivors. The proportional hazards assumption was checked using both statistical tests and graphical diagnostics based on the scaled Schoenfeld residuals.
Only variables having ≤20% missing data were considered to include in each Cox model after a multiple imputation process. Briefly, we used fully specified chained equations in the R package. 15  Mechanisms underlying missing data were investigated with sensitivity analyses. Ten imputed data sets were created and combined using between/within variance techniques to appropriately investigate uncertainty about the missing data. 15  Each model took intrinsic differences among centers using random effect into account. We report risk estimates as hazard ratios (HRs) with their 95% CIs and P values. We considered P<0. 05 as statistically significant, and hypothesis tests were 2-sided. All data were merged from deidentified files into SPSS 26. 0 (IBM, Armonk, NY) and R 4. 1. 2 (R Foundation for Statistical Computing, Vienna, Austria) for data management and statistical analysis.
Baseline, Surgical, and ECMO Characteristics In total, data on 2163 patients were collected in the PELS-1 database. Of them, 72 patients lacked data on the primary outcome and 33 received veno-venous ECMO support. Thus, 2058 patients were included in the present analysis (Figures  S2  and S3 ). Median age was 65. 0 years (IQR, 55. 0-72. 0 years), with women accounting for 41% (n=843;   Nonsurvivors were more often affected by valvular or aortic vessel diseases (Table  1 ), which was reflected by a higher percentage of concomitant procedures, aortic surgery, and valve surgery, but also by longer cardiopulmonary bypass and cross-clamp times (Table  2 ). Indications to start an ECMO support (Table  3 ) included failure to wean from cardiopulmonary bypass (n=788 [39. 2%]), followed by cardiogenic shock (n=506 [25.
Strengths and Limitations The structured data collection performed in the PELS-1, the participation of 34 centers from 16 countries, and the large sample size support data robustness and statistical power. Nevertheless, PELS-1 is observational by nature, preventing causal inferences. Data on how many adult patients received cardiac surgery at each center during the study period were not available because the analysis of ECMO implantation rates in cardiac surgery was beyond the aim of this study. Furthermore, specific data on ECMO selection criteria, protocols, weaning strategies, serial arterial lactate concentrations, longitudinal/serial data, vasopressor, and inotrope use are not captured by the database and could therefore not be included in this study. Furthermore, an in-depth analysis of intraoperative and postoperative hemodynamic parameters, as well as coagulation parameters, anesthesia management protocols, quality of life, and rehospitalization events after discharge, was not possible.
Septic shock was reported by each investigator according to the study definition. 36  However, codes for surgical site infection, bloodstream infections, antibiotics, and infectious agents are not present in the data set, and we cannot exclude a misdiagnosis of some patients who experienced persistent distributive shock or other kinds of shock accounting for persistent  hemodynamic failure. The local policies for left ventricular venting differed widely among participating centers, preventing any speculation on relationships between cardiac venting and enhanced myocardial recovery/ ability to wean off ECMO support. Finally, several clinical variables were collected but showed a significant amount of missing data (>20%) and were not included in the mixed Cox models.
Data collection The following predefined groups of data were collected: -Demographic data: age, sex, race Patients characteristics: EuroSCORE, length, weight, serum creatinine level, left ventricular ejection fraction, comorbidities (hypertension, chronic kidney disease requiring dialysis, previous myocardial infarction, previous endocarditis, smoking, previous stroke, atrial fibrillation, previous pulmonary embolism, diabetes mellitus, previous transient ischemic attack (TIA), implanted pacemaker (PM), implanted implantable cardioverter defibrillator (ICD), previous percutaneous coronary intervention (PCI), chronic obstructive pulmonary disease (COPD), peripheral artery disease, chronic pulmonary embolism, asthma, pulmonary hypertension, previous cardiac surgery, implanted left ventricular assist device (LVAD), NYHA class, -Preoperative status: urgency of the procedure, weight of intervention, planned intervention, preoperative cardiogenic shock, preoperative intubation, preoperative cardiac arrest, preoperative septic shock, preoperative vasopressors, preoperative acute pulmonary oedema, preoperative intraaortic balloon pump (IABP), preoperative right ventricular failure, preoperative biventricular failure -Diagnosis: coronary artery disease, aortic vessel disease, aortic valve disease, mitral valve disease, tricuspid valve disease, pulmonary valve disease, post-acute myocardial infarction (AMI) ventricular septal rupture, free wall/Papillary muscle rupture, graft failure, active endocarditis, atrial septal defect, post-LVAD right ventricular failure, other diagnosis.
Introduction One of the goals of asthma management is to prevent asthma exacerbations, which have been linked to accelerated lung function decline  [1]  and negative effects on quality of life  [2]  and contribute significantly to the economic burden of the disease  [3]. Past asthma exacerbations, particularly recent events, significantly and independently predict future risk  [4, 5]. Other characteristics, such as elevated type 2 inflammatory biomarkers, also contribute to increased future exacerbation risk  [6] [7] [8] [9] [10]. Dupilumab, a fully human monoclonal antibody, blocks the shared receptor for interleukin 4 (IL-4) and IL-13, key and central drivers of type 2 inflammation in multiple diseases  [11] [12] [13] [14]. In the phase 3 LIBERTY ASTHMA QUEST study (NCT02414854) add-on dupilumab vs placebo significantly reduced the rate of severe asthma exacerbations and improved pre-bronchodilator forced expiratory volume in 1 second (FEV1) in patients with uncontrolled, moderate-to-severe asthma  [15].
Treatment effects were greater in patients with elevated baseline levels of type 2 biomarkers, including blood eosinophils ≥150 cells•μL -1 and fractional exhaled nitric oxide (FeNO) ≥ 20 ppb  [15]. Transient elevations in blood eosinophil counts were seen at initiation of dupilumab treatment, with counts decreasing to close to baseline levels by Week 52, while mean FeNO levels declined over the study in patients treated with dupilumab  [15]. Blood eosinophils in dupilumab-treated patients continued to gradually decline to below QUEST study baseline levels by Week 4 of the TRAVERSE (NCT02134028) long term, single-arm open-label extension study  [16]. Safety findings from QUEST were consistent with the known dupilumab safety profile and have been previously reported  [15]. A subsequent analysis of QUEST study data showed that dupilumab improves clinical outcomes regardless of exacerbation history  [17] , but the long-term impact of dupilumab on clinical efficacy in patients with a history of exacerbations is unknown.
Patients We present here data from patients who had participated in QUEST and were subsequently enrolled in TRAVERSE, had experienced 1, 2, or ≥ 3 exacerbations in the year before enrolment in QUEST, and had baseline blood eosinophils ≥ 150 cells•µL -1 or FeNO ≥ 20 ppb at the time of enrolment in QUEST. These cut-off values of eosinophils and FeNO are in line with those suggested as indicative of type 2 asthma  [18]. To assess the long-term effect of continuous dupilumab treatment without confounding from dropout due to protocol amendment or different exposure durations, data from the subset of patients who had received 3 full years of treatment (i. e. completed the full 96-week treatment period in TRAVERSE, in addition to the 52week treatment period during QUEST) are presented in the supplementary material.
Results A total of 1279 (83. 6%) of 1530 patients from QUEST who enrolled in TRAVERSE were included in this post hoc analysis. Further details of both studies and the patient subgroups included in this analysis are found in Supplementary Figure  S1. PSBL characteristics for the patients who had experienced 1 (n=624), 2 (n=344), or ≥ 3 (n=311) exacerbations in the year before enrolment in QUEST are shown, by treatment group, in Table  1. The equivalent data for patients who had received treatment for a full 3 years are shown in Supplementary Table  S1. Patients' exacerbation history at PSBL was broadly reflective of their disease status, with generally poorer baseline pre-bronchodilator FEV1, asthma control (as assessed by ACQ-5), and asthma-related quality of life (Asthma Quality of Life Questionnaire [AQLQ]) as the number of exacerbations in the year before QUEST increased.
Type 2 inflammation is successfully targeted by dupilumab owing to its blockade of IL-4 and IL-13, key and central drivers of type 2 inflammation in multiple diseases. While the data were collected from large phase 3 clinical trials spanning a period of up to 3 years, exacerbation history subgroups were not pre-specified in QUEST and TRAVERSE. Therefore, a limitation of this analysis is its post hoc nature. Further discussion of limitations can be found in previous work  [17]. In summary, this analysis of patients with uncontrolled, moderate-to-severe asthma with a type 2 inflammatory phenotype showed that treatment with dupilumab can provide sustained, longterm reduction of exacerbation rates and improvements in lung function and asthma control irrespective of exacerbation history.
Acknowledgements Research sponsored by  Sanofi  and  Regeneron Pharmaceuticals Inc. ClinicalTrials. gov Identifiers:  NCT02414854  and  NCT02134028. Medical writing and editorial assistance were provided by  Jo Mooij, PhD , of  Excerpta Medica  and funded by  Sanofi  and  Regeneron Pharmaceuticals Inc. , according to the Good Publication Practice guideline.
This score was computed as follows: female gender, +1; current use of anxiolytics/antidepressant, +1; and perceived health status prior to admission: excellent or very good (0), good (+1), poor (+2), and bad (+3). To be eligible for enrollment in the study, a patient needed to score above the pre-defined threshold of 1 on the 3-item assessment procedure; this was designed to select patients at risk for PCLS. This score was developed using data from previous studies conducted in the ER setting of the Bordeaux Teaching Hospital. 8, 10, 19  Patients who were unable to provide written informed consent, unwilling to be contacted at 3 months, and/or under the influence of acute drug or alcohol use or dependence that, in the opinion of the site investigator, could interfere with adherence to study requirements were excluded from the study.
Materials and Methods We used data from the Paris Registry of Congenital Malformations (remaPAR), which records all live births and stillbirths at ≥22 weeks of gestation and TOPFA at any gestational age with congenital and/or genetic anomalies detected during pregnancy or in the neonatal period (ie, before discharge from hospital after delivery). This registry covers all births to mothers living in Paris and delivering in Parisian maternity units, representing approximately 25,000 births per year. Eligible cases with congenital anomalies are identified from medical reports in maternity units, specialized services (cardiology, neonatology, surgery), and cytogenetic and pathology units. Cases are also identified using hospital discharge data and the first medical certificates completed by pediatricians at hospital discharge.
Isolated minor anomalies, as defined by the EUROCAT network, are not eligible for inclusion (Supplemental Table  S1 ). 26  The registry also collects information on neonatal characteristics and outcomes from hospital data and vital statistics including data on infant mortality, defined as infant death in the first year of life. The study population included all singleton births with congenital anomalies recorded in the Paris Registry of Congenital Malformations from January 1, 2001 to December 31, 2021. We included all women undergoing routine prenatal screening or screening for indications other than those included in the recommended screening guidelines. We excluded multiple births because prenatal ultrasound screening for multiple pregnancies differs from that for singletons. In addition, cases with missing data on the timing of diagnosis were also excluded.
For each eligible case, data are collected using a standardized form on maternal sociodemographic and anthropometric characteristics, maternal and paternal medical history, and antenatal care including the results of the 3 routine trimester ultrasounds, results from screening for Down syndrome, and invasive prenatal testing. In France, 1 ultrasound is recommended in each trimester of pregnancy for all pregnant individuals at 11 to 14, 20 to 25, and 30 to 35 weeks of gestation. 24  During these ultrasounds, fetal anatomic examination is recommended following French guidelines, and includes the systematic assessment of the following fetal structures  9, 27, 28  : in the first-trimester ultrasound, the head, the neck, the face, the spine, the chest, the heart, the abdomen, the abdominal wall, and the extremities; in the second-trimester ultrasound, the upper lip, the fetal profile, and the presence of 4 limbs; and in both second-and third-trimester scans, the skull, the cerebral structures on an axial view of the fetal head at the level of the thalami and of the brain visualizing the cerebellum (including the cavum of the septum pellucidum, occipital horn of lateral ventricles, and posterior fossa), the heart (position, 4chamber view, and aortic and pulmonary outflow tracts examination), the stomach (presence and position), the kidneys (presence and aspect), the normal appearance of the intraabdominal structures/organs, the abdominal wall, the bladder, and the spine.
Results From the 17,405 births recorded in the Paris Registry of Congenital Malformations from January 1, 2001 to December 31, 2021, 748 multiple births and 55 cases with missing data on the timing of diagnosis were excluded, leaving a study population of 16,602 births. A total of 523,639 singleton births from mothers residing and delivering in Paris were recorded in the Paris area during the study period, and 3. 2% of these births were found to have congenital anomalies. Of the 16,602 malformed singleton fetuses and newborns, 5426 (32. 7%) were not detected to have congenital anomalies during pregnancy. A total of 2272 fetuses and newborns (13. 7%) had 1 of the 11 congenital anomalies considered as severe, and 11. 9% of these cases were not detected prenatally. Figure  1  presents the proportion of congenital anomalies detected postnatally according to the EUROCAT   Severe congenital anomalies had the lowest postnatal detection rates: 0.