dataesr/scientific-paragraphs-categorization huggingface dataset:
Background: The CELESTIAL trial (NCT01908426) demonstrated overall survival benefit for cabozantinib versus placebo in patients with advanced hepatocellular carcinoma (aHCC) who had received prior sorafenib treatment. This analysis of CELESTIAL compared the impact of cabozantinib versus placebo on health-related quality of life (HRQoL). Materials and methods: Health status was assessed using the EuroQol five-dimension five-level (EQ-5D-5L) questionnaire over the 800-day follow-up period. EQ-5D-5L health states were mapped to health utility scores using reference values for the UK population. Qualityadjusted life years (QALYs) were calculated for each treatment group as the area under the curve for the plot of health utility score over time. The between-treatment group difference in restricted mean QALYs was calculated by generalized linear models and adjusted for baseline differences.
Utility values are applied to an EQ-5D-5L health status recorded for a population receiving a particular treatment to provide an overall measure of the 'health utility' of that treatment [8e10]. Cabozantinib inhibits multiple receptor tyrosine kinases, including vascular endothelial growth factor receptors, the GAS6 receptor (AXL), and the hepatocyte growth factor receptor protein (MET)  [11, 12]. Based on findings from the pivotal phase 3 CELESTIAL trial (NCT01908426)  [3] , cabozantinib is approved in Europe and the USA for the treatment of patients with HCC who have been previously treated with sorafenib  [11, 12]. This analysis of the CELESTIAL trial evaluated the impact of cabozantinib compared with placebo on HRQoL in terms of health utility and QALY assessment.
e. , last visit, censorship or death) to the end of the follow-up period (i. e. , until death or censorship, up to a maximum of 41 months post randomisation). Where the last recorded status was death, a value of zero was applied and carried over. Where the patient's last status was censorship, their last recorded utility value was carried over until the end of follow-up. The second analysis method e 'analyses excluding mortality' e only used direct measures of utility (obtained while patients were alive); no follow-up data were inferred and carried over.
A generalized linear model with identity link and Gaussian error was used to analyse the restricted mean QALYs accrued; the summed health utility score was used as the response variable, and the randomization group was used as the explanatory factor. When the model was adjusted for baseline differences, baseline health utility score was included as an additional patient-level explanatory variable. All analyses were undertaken using SAS software, version 9. 4 (SAS Institute Inc. , Cary, NC, USA).
Although cabozantinib was associated with an initial reduction in the health utility score compared with placebo, patients receiving cabozantinib benefited from longer overall survival and greater QALY accrual over time. The difference in restricted mean QALY accrual was statistically significant and similar or greater than the MID, supporting a positive benefiterisk profile for cabozantinib. Further analyses by line of therapy were not possible due to insufficient sample sizes. The sensitivity analysis using the Devlin reference utility value set showed a greater HRQoL benefit (in terms of health utility score and accrued QALYs) with cabozantinib than was shown in the primary (crosswalk) analysis. This may be because the Devlin value set allows full use of the EQ-5D-5L health-status data while the crosswalk values have been adapted from an EQ-5D-3L value set.
However, NICE have raised concerns over the validity of the source data used to generate the Devlin value set  [9]. Two approaches were used to estimate accrued QALYs for patients who died during the trial; both showed a benefit of cabozantinib over placebo, but both may be subject to potential bias. The method that excluded mortality has the potential to overestimate the treatment effect of cabozantinib on QALYs, while the analysis that included mortality has the potential to underestimate the full QALY accrual (versus placebo) of treatments with proven survival benefit. The latter (more conservative) approach is recommended by NICE and goes some way to mitigating the potential overestimates in the analyses excluding mortality  [6]. The results of this HRQoL analysis are limited by the early cessation of CELESTIAL for clinical benefit, which resulted in a truncated follow-up period and the need to 'project' health state values for the remainder of the outcome period for a substantial number of patients.
Acknowledgements The authors thank  Oxford PharmaGenesis ,  Oxford, UK , who provided editorial support, which was sponsored by  Ipsen  in accordance with Good Publication Practice 3 (GPP3) guidelines. The authors also thank all patients involved in the study, as well as their caregivers, care team, and the investigators and research staff in the participating institutions. Tim Meyer is part funded by the  UCLH Biomedical Research Centre.
Participants Data were available from Leuven University Hospital. The study was approved by the Institutional Ethical Board of UZ Leuven. Consent forms were signed by parents of participants 17 years of age or subjects ≥18 years old. For 20 children and adolescents, aged between 5 and 22 years, diagnosed with Duchenne muscular dystrophy, a direct measurement of GFR was performed. [20] GFR was measured in these patients because current estimating GFR equations based on creatinine are particularly inaccurate in patients with such muscle disease.
Simulations. Using Microsoft Excel's random number generator (RAND() provides a random number between 0 and 1), we simulated 3000 new concentration-time decays per subject, with data around the original data, by randomly generating time-points, concentrations, and injected activity deviating from the original data by no more than ±2. 5%, that is, New Value = Original Value Â (1 + r) with r = [À2. 5 + 5. 0 Â RAND ()]/100. We fitted each of these 3000 datasets (for each subject), using the Slope-Intercept method (but discarding the negative subtractions) and calculated the GFR.
Discussion In the current study, we measured the full plasma disappearance curve after bolus injection of 51 Cr-EDTA in 20 Duchenne muscular dystrophy patients. Most of these children have BSA < 1. 73 m 2 and the indexed GFR is mostly high. Because we experienced problems when fitting the 2 compartment curve with the slope-intercept method (6 out of 20 curves could not be analyzed with the SI-method), we evaluated 8 different fitting procedures. Our analysis demonstrates the flaws of each fitting procedure. Goodness of fit as measured by the sum of squares of residuals will always result in the best fit for the NLLS full  compartment model, but this does not guarantee the accuracy of the reported GFR. The slope-intercept method fails to fit the curve in (too) many occasions. The weighted NLLS, weighted S-NLLS, and weighted mS-NLLS seemed to be the most robust fitting procedures for the data at hand, but also these methods failed in some occasions to report a reliable GFR-result.
Results Sample characteristics. 54 samples were collected for this study, with patient demographics summarised in Table  1. Sample size was limited by availability of MIS-C and COVID-19 ARDS patients, whose clinical characteristics are listed in Supplementary Data 1. SWATH acquisition of the 54 samples were obtained, and then matched to the local assay library for quantitation of peptide areas, resulting in identification of 319 proteins. SWATH-MS passed quality control, interquartile coefficient of variance (CV) between pooled sample, commercial control and technical replicates were below 10% with a median CV of 3%. No MIS-C patients had any pre-existing conditions. 1 COVID-19 ARDS patient had sickle-cell anaemia and another COVID-19 ARDS patient had leukaemia. At the time of blood collection, all but two MIS-C patients had received IVIG in the past 48 h, and eight had received steroid treatment in the past 48 h.
Methods Participants recruitment. This study was approved by the Royal Children's Hospital Ethics in Human Research Committee, reference number 34184; and the Necker Hospital, France, reference number Assistance Publique -Hôpitaux de Paris-registration N°. 2020 0428163907 and Trial Registration: NCT04420468. Written informed consent was obtained from the parent or guardians of healthy child participants and participants were not compensated for this study. Patient criteria of COVID-19 ARDS and MIS-C were respectively defined according to the interim guidance of World Health Organization for novel coronavirus  10  and The Paediatric Acute Lung Injury Consensus Conference  38. Blood samples from healthy children were collected, processed and stored at -80 °C prior to the COVID-19 pandemic, from children attending day-surgery at the Royal Children's Hospital, Australia, as part of the HAPPI Kids study  39.
In IDA mode, a TOFMS survey scan was acquired at m/z 350-1500 with 0. 25 s accumulation time, with the twenty most intense precursor ions (2 + -5 + ; counts > 200) in the survey scan consecutively isolated for subsequent product ion scans. Dynamic exclusion was used with a window of 30 s. Product ion spectra were accumulated for 100 ms in the mass range m/z 100-1800 with rolling collision energy. IDA Data Analysis-Protein identifications from 2D-IDA data were performed with ProteinPilot (v5. 0, Sciex) using the Paragon algorithm in thorough mode. The search parameters were as follows: sample type: identification; cys alkylation: iodoacetamide; digestion: trypsin; instrument: TripleTOF 6600; special factors: none; species: Homo sapiens; ID focus: biological modifications. The database used was obtained from SwissProt (20,420 entries, May 2019) with the addition of a coronavirus database downloaded from UniProt (reviewed, 520 entries, October 2020).
A reversed-decoy database search strategy was used with ProteinPilot, with the calculated protein FDR equalling 1%. SWATH Library Construction-The ProteinPilot group file from the 2D-IDA search result was imported into PeakView (v2. 2) (Sciex) and used as a local peptide assay library. This library contained 387 proteins identified from un-depleted plasma samples. SWATH-MS-For SWATH-MS experiments 10 μL of each sample (5 μL of digested sample diluted with 5 μL of loading buffer) was injected in sample order one batch at a time, with one sample from each batch randomly selected and acquired twice. Identical LC conditions were used as described above, with m/z window sizes determined based on precursor m/z frequencies in previous IDA data. SWATH variable window acquisition with a set of 100 overlapping windows (1 amu for the window overlap) was constructed covering the mass range of m/z 399.
5-1249. 5. In SWATH mode, first a TOFMS survey scan was acquired (m/z 350-1500, 0. 05 s) then the 100 predefined m/z ranges were sequentially subjected to MS/MS analysis. Product ion spectra were accumulated for 30 ms in the mass range m/z 350-1500 with rolling collision energy optimised for lowed m/z in m/z window +10%, with the total duty cycle of 3. 09 s. SWATH-MS Quality Control-One sample from each batch was randomly selected and acquired twice. In addition, a commercially available human plasma which was used as a control plasma standard sample (ST01) was acquired once at the beginning, the pooled sample acquired in duplicate once at the beginning and once at the end of the series. Quality control samples and technical replicate samples were run and extracted together with the samples to assess the overall data quality.
Total area normalisation was applied. Data quality was evaluated using PCA, and CV was calculated for all quality control samples and technical replicates. SWATH data analysis. SWATH peaks were extracted using PeakView (v. 2. 2). Retention times of peptides between the 2D-IDA and SWATH analysis were aligned with the aid of endogenous peptides using the retention time alignment feature of PeakView software. The Shared and modified peptides were excluded. Peak extraction parameters were set as the following: 100 peptides per protein, 6 transition ions per peptide, peptide confidence threshold 99%, FDR extraction threshold 1%, XIC (Extract Ion Chromatogram) retention time window 10 min and mass tolerance 75 ppm. The extracted transition ion peak areas, peptide peak areas and protein peak areas were exported in Excel for further statistical analysis.
Statistical analysis. Quantitative MS data was obtained from 54 independent biological samples for the three groups (20 Health, 5 COVID-19 ARDS and 29 MIS-C). Statistical analysis was performed using similar methods that are described in  39. In brief, the peptide ion peak areas were averaged for the replicate technical injections, then further scaled so that the sum of the ion intensities of each sample equal the maximum total ion intensity. The normalised ion intensities were summed for each peptide and protein. The entire protein level data was clustered using hierarchical clustering (correlation distance and complete linkage) and visualised using heatmaps. Multivariate analysis such as PCA was used to examine global trends among sample groups. Two approaches were undertaken to determine differential expression between groups: an analysis of variance on the log-transformed normalised protein peak areas, and un-paired t test between individual groups.
Acknowledgements This study was funded by the  Royal Children's Hospital Foundation, Melbourne, Australia  grant reference ' 2020-1283 COVID-19  and blood clotting in children' awarded to PM. Aspects of this work was conducted at the  Australian Proteome Analysis Facility  and supported by the  Australian Governments National Collaborative Research Infrastructure Scheme (NCRIS). The authors thank staff from the  Pathology Collection Department at The Royal Children's Hospital  for obtaining the consent of healthy participants and the collection of samples. The authors thank the haematology laboratory from  Necker Hospital, France , for sample collection. We also thank members of the anaesthetic and surgical departments of the  Royal Children's Hospital  for facilitation of sample collection.
Data availability The raw complete mass spectrometry proteomics data generated in this study been deposited to the ProteomeXchange Consortium database via the PRIDE partner repository with the dataset identifier PXD025125. This can be accessed using the following link (https://www. ebi. ac. uk/pride/archive/projects/PXD025125). The SwissProt identifiers that support the data in this manuscript are publicly available from the uniprot database https://www. uniprot. org/uniprot/?query=reviewed:yes and FASTA file can be downloaded https://ftp. uniprot. org/pub/databases/uniprot/current_ release/knowledgebase/complete/uniprot_sprot. fasta. gz. Supplementary data 2-4 report specific Uniprot Accession IDs for each protein discovered in this manuscript. These uniprot accession IDs were used for further pathway analysis.
, 2009; Higgins & Vander Zanden, 2010; Phelps, 1994) , which may benefit certain recreational activities (e. g. , scuba diving and angling), while at the same time causing food web disruptions that harm fisheries  (Kao et al. , 2016). Despite the notoriety of invasive freshwater bivalves in invasion science, information on their economic costs has not been synthesized. To broadly address this pervasive lack of information and provide a basis for quantifications of costs associated with invasive species worldwide, the InvaCost database has recently been developed  (Diagne et al. , 2020). This database contains extensive information on the costs (e. g. , cost types, impacted sectors, regional attributes, cost estimation reliability) associated with ~500 invasive species. In the present study, we use a subset of the database to describe global costs associated with invasive freshwater bivalves and how they are structured, anticipating unevenness in cost reporting towards a few regions and a few highly conspicuous invasive species.
| Original data To estimate the cost of bivalve invasions of freshwaters on the global economy, we considered data from the latest version of the InvaCost database at the time of writing this manuscript (version 4. 0; full database and descriptive files are available at https://doi. org/10. 6084/ K E Y W O R D S Cyrenidae, Dreissenidae, InvaCost, macrofouling, mussel, non-native, socio-economic impact m9. figsh are. 12668570). We note that although some IAS can provide both benefits and costs to economies  (Kouranditou et al. , 2022) , InvaCost does not quantify benefits provided by IAS and potential beneficial effects are thus outside the scope of this study. This database (13,123 entries;  Diagne et al. , 2020; Angulo et al. , 2021)  compiles entries that extensively describe documented costs globally, enabling large-scale cost syntheses associated with invasive species in different spatial and temporal frames.
Grey and published references were retrieved from standardized searches in online repositories (Web of Science, Google Scholar and Google search engine) and opportunistic collection based on targeted searches. Full information on the search terms (see Appendix S1) is provided in  Diagne et al. (2020)  and  Angulo et al. (2021). Gathered references were thoroughly examined to assess relevance and then scrutinized for collating cost estimates associated with invasive species. Every cost entry was recorded, depicted by 64 parameters, and finally converted to a common and upto-date currency (US dollars (US$) 2017; see  Diagne et al. , 2020, for  detailed information; Appendix S2). From this full database, 241 cost data entries were identified as exclusively belonging to the Bivalvia class using the "Class" column filter and 233 cost data entries belonging to bivalves which impact freshwaters (see Figure  1 ).
This was done by "expanding" the database via the expandYearlyCosts function of the "invacost" R package  (Leroy et al. , 2021 )-a process that causes each entry in the database to correspond to a single year, thereby increasing the number of entries beyond that of the original data. For example, an initial single cost between 2000 and 2009 that totalled at $ 10,000 would become ten entries at $ 1,000 each after the expansion. All analyses were performed using this version of the database. A full explanation of this and other functions used is available in  Leroy et al. (2021). For one cost entry, the probable ending year was presumably after 2020. Hence, all resulting cost estimates projected beyond 2020 were not taken into account. Similarly, costs were not available before 1980. This resulted in a subset of 461 expanded database entries (Figure  1 ).
The dataset was then reduced to 461 entries by removing entries before 1980 to ensure comparability of currency translations ("recent" in Figure  1 ) and is provided in Appendix S3. or with documented, repeatable and/or traceable methods were designated as high reliability; all other estimates were designated as low reliability  (Diagne et al. , 2020). We acknowledge that this approach, which categorizes costs as high reliability based on their presence in peer-reviewed or official material, may not be fully representative of the diverse forms of method reliability of cost estimates. Nevertheless, these criteria provided clear, objective and reproducible means of assessing material, as it was not feasible to assess method reliability on a broader categorical scale; (ii) Implementation: referring to whether the cost estimate was actually realized in the invaded habitat (observed) or whether it was extrapolated (potential), based on the methods reported in the underlying study (i.
g. , the golden mussel L. fortunei, the false mussel Mytilopsis trautwineana, the Chinese pond mussel Sinanodonta woodiana) account for only a few entries in the InvaCost database, owing to a lack of published or traceable cost data. Macrofouling induced by L. fortunei and M. leucophaeta (a predominantly brackish-water species that was not represented in InvaCost), in particular, has been recognized as an economic problem for South America and Europe, respectively, where they foul municipal and industrial water supply systems  (Verween et al. , 2010 ). Yet, their invaded regions contributed very little or none of the total documented costs of freshwater invasive.
, the invasion costs were specifically estimated by summing all entries according to different descriptive columns of the database (see Appendix S2): F I G U R E 1 Successive steps of filtering from the entire InvaCost database to the subset analysed for annualized costs of freshwater bivalves between 1980 and 2020. Each step is detailed in the text (i) Method reliability: illustrating the perceived reliability of cost estimates based on the type of publication and method of estimation. Estimates in peer-reviewed publications or official reports,.
; Appendix 3). The collective costs of the 461 expanded entries in the InvaCost database for freshwater bivalves amounted to $ 63. 7 billion covering the impacted years 1980-2020. At the family level, 381 cost entries were attributable to Dreissenidae ($ 51. 1 billion), 28 to Cyrenidae (formerly Corbiculidae; $ 12. 4 billion), 46 to Mytilidae ($ 140. 5 million) and two to Unionidae ($ 16. 4 thousand). Four cost entries were inferred by diverse families of bivalves ($ 9. 3 thousand). Within Dreissenidae, 380 cost entries were linked to the zebra mussel D. polymorpha ($ 19. 3 billion; n = 271) and quagga mussel D. bugensis ($ 46. 4 million; n = 2), either singularly or in congeneric combination ($ 31. 7 billion; n = 107), as well as one entry for the false mussel (Mytilopsis trautwineana $ 68.
Within the InvaCost database, Dreissenidae constituted the majority of data sources and costs, while fewer cost entries referred to Cyrenidae and none to other families, excepting minor additions from Mytilidae and Unionidae. Within these families, D. polymorpha, D. bugensis and C. fluminea were implicated in the vast majority of economic damages, particularly in North America where they are widespread and locally abundant. Nonetheless, species such as C.
Annual costs between 1980 and 2020 for invasive macrofouling freshwater bivalves (a) and total number of cost reporting references (i. e. number of studies) per decade (b). On (a), black squares and adjacent bars represent decadal means; blue circles represent annual total costs and are scaled according to the number of database entries (i. e. number of costs within studies). Note the broken y-axis on a log 10 scale in (a) for example, by not distinguishing particular taxa causing the costs, or sectors in which they are incurred.
Finally , the invasion costs were specifically estimated by summing all entries according to different descriptive columns of the database (see Appendix S2): F I G U R E 1 Successive steps of filtering from the entire InvaCost database to the subset analysed for annualized costs of freshwater bivalves between 1980 and 2020. Each step is detailed in the text (i) Method reliability: illustrating the perceived reliability of cost estimates based on the type of publication and method of estimation. Estimates in peer-reviewed publications or official reports,.
F Annual costs between 1980 and 2020 for invasive macrofouling freshwater bivalves (a) and total number of cost reporting references (i. e. number of studies) per decade (b). On (a), black squares and adjacent bars represent decadal means; blue circles represent annual total costs and are scaled according to the number of database entries (i. e. number of costs within studies). Note the broken y-axis on a log 10 scale in (a) for example, by not distinguishing particular taxa causing the costs, or sectors in which they are incurred.