dataesr/scientific-paragraphs-categorization huggingface dataset:
For 5-FU resistance out of 13 upregulated and 14 downregulated genes listed by Zheng et al. , we found that 4 (ABCC3, FOX O 3, LGALS3, and SULF2) were upregulated, while 5 (SLC19A1, FANCA, RRM1, MTAP, and NF2) were downregulated in our dataset (30 and 35%, respectively). ABCB1 was also upregulated (1. 9-fold, adj. p 1. 9 × 10 -5 ) in 3D bioprinted cells, and this gene codes for a pump widely implicated in 5-FU resistance  (Wang et al. , 2015; Blondy et al. , 2020). For irinotecan (or its active form SN-38), we found 6 upregulated genes (SRC, CSF1R, LOX, ERBB3, SAT1, and LDLR) out of 22 listed genes, and only 1 downregulated gene (MSH3) out of 24 listed (27 and 4%, respectively). Last, for oxaliplatin, we detected only 2 upregulated genes out of 14 (CD24 and RICTOR) and 5 downregulated genes (DHFR, EFIF4E, LYN, TYMS, and MTAP) out of 30 (14 and 17%, respectively).
ACKNOWLEDGMENTS We would like to acknowledge financial support by  Amgen Bulgaria  (collaboration agreement with  MU-Plovdiv No 25-05  to 58), the  Medical University of Plovdiv  (project No  DPDP-01/ 20 ), and the  National University Complex for Biomedical and Applied Research , with participation in  BBMRI-ERIC" (NUCBPI-BBMRI. BG ), within the national road map for research infrastructure (Contract No  DO1-395/December 18 ,  2020 ), and the  IMODI  and  3DONCOCHIP  consortium funding of the national and regional French governments. We would also like to acknowledge  CELLINK (Sweden)  for their technical support with bioprinting, Novogene,  United Kingdom , for RNA-sequencing and initial data analysis, and  Slavka Raynova  ( MU-Plovdiv ) and  Anelia Rachevska  ( UMHAT-Eurohospital, Plovdiv ) for their laboratory technical support with sample preparation.
FUNDING The work carried out at the  Medical University of Plovdiv  was supported by  Amgen Bulgaria  (collaboration agreement no  25-05-58 ), the  Medical University of Plovdiv  (project no  DPDP-01/ 20 ), and the  National University Complex for Biomedical and Applied Research , with participation in  BBMRI-ERIC" (NUCBPI-BBMRI. BG ), within the national road map for research infrastructure (contract no  DO1-395/18. 12. 2020 ), while  Clement Milet  and  Prof. Colin McGuckin  were funded by the  IMODI  and  3DONCOCHIP  consortium funding of the national and regional French governments.
Acknowledgements We want to thank  Virginie Hivert ,  Concha Mayo  and  Zachary Hallinan  for their significant contributions to the development of the patient engagement sustainability roadmap, and to  Daniel O'Connor ,  Colin Attrill ,  Mike Dykes ,  Maria Mavris ,  Nathalie Bere ,  Lenita Lindström-Gommers ,  Andrea Furia-Helms  and  Agnès Saint-Raymond  for their time and insights during the interviews conducted to populate the roadmap. In addition, we would like to thank all PARADIGM work package 6 partners for their engagement and support throughout the development of the sustainability strategy.
INTRODUCTION Remdesivir was granted emergency use authorized by the US FDA and approved as the first drug for the treatment of patients with COVID-19  [1, 2]. These approvals have accelerated demand for remdesivir worldwide, but potential toxicities associated with this drug remain largely unknown and warrant urgent safety evaluation. Altough hepatotoxicity is one of the most frequent complication observed in COVID-19 patients, the association between remdesivir and gastrointestinal adverse drug reactions (GI-ADRs) in RCT is conflicting to date  [2] [3] [4] [5] [6]. Furthermore, elevated liver enzymes as part of the clinical course in certain COVID-19 patients complicated the differentiation of GI-ADRs induced by remdesivir from clinical course of COVID-19. In this study, we aimed to detect a diverse spectrum of GI-ADRs associated with remdesivir, using the VigiBase, WHO's international pharmacovigilance database of individual case safety reports (ICSRs).
GI-ADRs associated with remdesivir in the full database from VigiBase. IC and IC025 of GI-ADRs associated with remdesivir compared in the entire database from VigiBase from inception from November 14, 1967 to August 30, 2020. † ROR with 95% CIs of GI-ADRs associated with remdesivir compared in the entire database from VigiBase from February 1 to August 30, 2020.
Table. 1. GI-ADRs associated with remdesivir in the full database from VigiBase. IC and IC025 of GI-ADRs associated with remdesivir compared in the entire database from VigiBase from inception from November 14, 1967 to August 30, 2020. † ROR with 95% CIs of GI-ADRs associated with remdesivir compared in the entire database from VigiBase from February 1 to August 30, 2020. Intestinal perforation 1 (0. 05) 6,872 (0. 03) 0. 40/-3. 40 231 (0. 02) NA Epigastric discomfort Constipation Values are n (%) unless otherwise indicated. Remdesivir 1 (0. 05) 1 (0. 05) Full database (since inception) * 10,133 (0. 04) 206,993 (0. 91) IC/IC025 0. 06/-3. 74 -3. 71/-7. 51 Full database 732 (0. 05) 13,470 (0. 96) (since Feb 2020)  † NA ROR (95% CI) NA Total numbers of individual case safety reports First reports of ADRs associated with remdesivir started in February 1, 2020 2,107 22,728,189 1,403,532 Alanine aminotransferase increased 371 (17.
Fourier Transform Infra-Red (FTIR) Spectroscopy The FTIR spectra of pure SLC and their solid dispersions (SLC-K188, SLC-K30, and SLC-KVA64) were recorded using an FTIR spectrometer (Jasco FTIR Spectrophotometer, Tokyo, Japan). Each sample was ground with crystalline potassium bromide using a glass mortar and pestle into a very fine particles, and pressed into transparent film. The transparent film was kept on a sample holder and the spectra was recorded using spectra manager software (Jasco, Tokyo, Japan)  [33].
Then, the cells were washed three times with TBS before stained with DAPI (3 µg/mL in TBS) for 10 min at room temperature. Finally, the filter membranes were cut from the Transwell ® and mounted between microslides. The immunofluorescent staining images of cell confluency and tight junction structure were characterized by Leica TCS SP8 laser-scanning confocal microscope (Leica Microsystems, Heidelberg, Germany) with the excitation and emission wavelength of 488 and 520 nm, respectively, for tight junction protein ZO-1 and adherens junction protein β-catenin, and 405 nm and 461 nm, respectively, for cell nuclei. The software Leica Application Suite X was used for 3D visualization. TEER = (R total -R blank ) × A (1) when R total is the measured resistance (Ω), R blank is the arithmetic mean of the resistance of blank Transwell ® insert (110 Ω), and A is the area of Transwell ® filter (1.
Statistical Analysis The experiments were performed at least in triplicate. For statistical comparison, the Kruskal-Wallis test with uncorrected Dunn's multiple comparison test was the method of statistical analysis. p-value of less than or equal to 0. 05 was considered statistically significant. All statistical analysis was performed using Prism GraphPad (version 8. 4. 1, GraphPad Software, San Diego, CA, USA).
Funding: This research was funded by  Agencia Estatal de Investigación   PID2019-104084GB-C21/AEI/ 10. 13039/501100011033  and  Junta de Extremadura-Fondo Europeo de Desarrollo Regional  (FEDER; Grants  IB20007  and  GR18061 ) to J. A. R. , I. J. and J. J. L. are supported by contracts from  Junta de Extremadura  ( TA18054  and  TA18011 ). N. P. and C. D. have been supported by  INSERM, Ligue Nationale Contre le Cancer, Ministère de l'Education Nationale, Hauts de France , C. D. is supported by contract from  I-SITE ULNE.
HF NMR analysis HF 1 H NMR experiments were performed on a Bruker Avance 400 spectrometer (Bruker Biospin AG, Fallanden, Switzerland) equipped with a 5 mm triple resonance probe (TXO) with 19 F direct detection. Typical acquisition parameters optimized for quantitative experiments were as follows: number of scans (NS) 128, pulse width 4. 52 µs (flip angle 30°), acquisition time 3. 40 s, spectral width 12 ppm, 32K data points, and an additional relaxation delay of 10 s; the recording time was thus ca. 29 min. The signal of TSP set at 0 ppm was used as an internal reference for chemical shift measurement and quantitative analysis. Three samples with lower concentrations of SCs (1-3) were recorded by increasing the NS to 1024 for obtaining a correct signal-to-noise (S/N) ratio. HF 1 H NMR data were processed using the Bruker TopSpin 4.
LF NMR analysis Spectra were acquired on a Pulsar TM benchtop NMR spectrometer (Oxford Instruments, Abingdon, UK) operating at a frequency of 59. 7 MHz for 1 H and 56. 17 MHz for 19 F. The temperature inside the spectrometer was 310 K. Acquisition was performed with the SpinFlow 1. 2. 0. 1 software (Oxford Instruments) and data processing with the MNova 11. 0 software (Mestrelab Research, Santiago de Compostela, Spain). LF 1 H NMR spectra were recorded with a flip angle of 90° (12. 5 µs), a spectral width of 5000 Hz, and 8K data points (acquisition time of 1. 64 s). The relaxation delay was set at 2 s, and 1024 transients were recorded leading to a total acquisition time of ≈1 h. For data processing, the FIDs with one level of zero-filling were apodized with an exponential (0. 3 Hz) filter and an automatic Whittaker smoother baseline correction was applied.
LF 19 F NMR spectra were recorded with a flip angle of 90°, a spectral width of 20833 Hz, and 32K data points (acquisition time of 1. 57 s). The relaxation delay was set at 1 s, and 2048 transients were recorded leading to a total acquisition time of 1 h 27 min. For data processing, the FIDs were apodized with an exponential (3. 0 Hz) filter and an automatic baseline correction was applied.
Protein ID and Data analysis Proteins were identified by comparing all MS/MS data with the proteome database of the complete reviewed proteome of Rattus norvegicus (Uniprot, release July 2018; 8,054 entries), using the MaxQuant software version 1. 6. 1. 0 [36, 37]. Lys-C trypsin specificity was used for the digestion mode with two missed cleavages. Carbarmidomethylation of cysteines was set as a fixed modification. Nterminal acetylation and methionine oxidation were selected as the variable modifications. For MS spectra, an initial mass tolerance of 6 ppm was selected, and the MS/MS tolerance was set to 20 ppm for HCD data  [38]. For identification, the FDR at the peptide spectrum matches (PSMs) and protein level was set to 0. 01. Relative, label-free quantification of proteins was performed using the MaxLFQ algorithm integrated into MaxQuant with the default parameters  [39].
Analysis of the proteins identified was performed using Perseus software (http://www. perseus-framew ork. org/) (version 1. 6. 2. 1)  [40]. The file containing the information from identification was used with hits to the reverse database, and proteins identified with modified peptides and potential contaminants were removed. Then, the LFQ intensity was logarithmized (log2[x]). Categorical annotation of rows was used to define different groups depending on the concentration of PCs inhibitor used (0, 50, 100, 150 µM). Multiple-samples tests were performed using an ANOVA test with a p-value of 0. 05 and preserved grouping in randomization. The results were normalized by Z-score and represented as hierarchical clustering. Functional annotation and characterization of identified proteins were obtained using PANTHER software (version 14.
Sub-network enrichment pathway analysis Using Elsevier's Pathway Studio (version 11. 0/ /Elsevier), all relationships between the differentially expressed proteins among all conditions were depicted based on the Ariadne ResNet  [41]. For proteins identified in the shotgun analysis post-stimulation of cell lines with PCs inhibitor, the Subnetwork Enrichment Analysis (SNEA) algorithm was used to detect the statistically significant altered biological pathways in which the identified proteins are involved. This algorithm uses Fisher's statistical test to detect any nonrandom associations between two categorical variables organized by a specific relationship. Also, this algorithm starts by creating a central "seed" from all the relevant identities in the database and builds connections with associated entities based on their relationship with the seed.
Quantification of spheroid size and invaded area After the spheroids were embedded, cell invasion of the spheroid was monitored by digital photography using a Leica DM IL LED Fluo inverted light microscope (Leica DFC450C camera) at room temperature, with the Leica Application Suite (LAS V4. 4). Images were acquired every day (day 0 = time of embedding in collagen; pictures were taken immediately after embedding) using a 4x/0. 10 objective. Image processing and quantification of spheroids and invasion areas were performed using in-house software. This in-house software takes into account cell density and not the limits of cell migration in the collagen matrix, which is observed independently. The implemented algorithm uses local fluctuations of the image intensity for automated estimation of the invasion magnitude.
8 ). To confirm these results, we analyzed the expression of pro-inflammatory and anti- inflammatory markers in the proteomic data (Supplementary Fig. 2 ). A decrease of anti-inflammatory expression is measured for CD206, STAT3 and Arginase 1 in macrophages treated with the conditioned medium from Poly (I:C)-treated macrophages with and without the addition of the PCs inhibitor. We also observed a higher expression of the pro-inflammatory marker MHC I in these macrophages even when they are associated with glioma cells in spheroids. We finally confirmed on spheroids made of NCH82 human glioma cells that the Poly (I:C) and PCs inhibitor are highly efficient on tumor invasion decrease 4 days after treatment (Supplementary Fig. 3 ).
e. pharmaceutical use versus environmental), leading to potential underestimations of actual exposures. Exposure underestimations may have large implications as they could fundamentally compromise outcomes of risk assessment decisions. In this study, we address the key issue of potential underestimation of APAP use using current human-biomonitoring methods focused on free APAP and phase II conjugates, only. We monitored APAP exposure over four days in blood and urine of four male volunteers with an administration of 1 g of APAP on the third day. Using recent untargeted technological advancements of high-resolution mass spectrometry (HRMS) combined with omics-based data treatment, we investigated the presence of neglected APAP metabolites from the enterohepatic circulation with formation and excretion times potentially longer than APAP phase II metabolites.
As a first step, all the samples were analysed in full scan experiment (50-1100 Da) in bothand + ESI modes. The common parameters for the full scan experiment in both positive/negative ion modes were: collision energy, 10 eV; curtain gas, 35; ion source gas 1, 50; ion source gas 2, 70; temperature, 550 • C; declustering potential, 80 V; mass resolution of 50,000. As a second step, MS/MS mass fragmentation information for chemical elucidation was obtained by further analysis of selected samples in sequential window acquisition of theoretical mass spectrum (SWATH). Selection of samples were made based on the presence of markers discriminating between the 3 groups (baseline group, 1-6 h group and + 1-2 days group, see HRMS dataset section) that needed further structural MSMS confirmation. Swath data-independent acquisitions were performed in order to achieve comprehensive MS/MS sampling.
The Q1 isolation window strategy was generated using the Sciex Variable Window Table from previously acquired MS data on the specific sample of interest in order to optimize window widths across the entire m/z range. SWATH experiments were performed in bothand + mode with parameters: MS1 accumulation time, 80 ms; MS2 accumulation time, 30 ms; collision energy, 35 eV; collision energy spread, 15 eV; cycle time, 469 ms; mass range, m/z 50-900. 2. 1. 4. 4. Quality control. For the untargeted analysis, one workup blank sample (i. e. , extraction with HPLC grade water instead of sample) per analytical batch was prepared to monitor for background contaminants. Quality control (QC) samples comprising a composite sample were prepared in order to monitor for UHPLC-ESI-TOF-MS repeatability and sensitivity during analysis of a sample run.
, 2017)  to correct mainly for urinary dilution issues. 2. 1. 4. 5. HRMS dataset. Mass spectra collected in full scan mode (m/z 50-1100) and spectral peaks were preprocessed (i. e. , deconvolution, alignment, peak picking) using both the open source R package XCMS  (Smith et al. , 2006)  and vendor software MarkerView 1. 3. 1 (AB, Sciex) to create separate aggregate datasets from all sample files in which the MS features were binned into retention time (Rt) × m/z values. For XCMS, the raw data files were first converted to 64 bit. mzML (full scan) using MSConvert from ProteoWizard  (Adusumilli and Mallick, 2017). The peak tables obtained with XCMS were used for multivariate and univariate analyses in an R environment (R version 3. 6. 0) in order to identify APAP metabolites after APAP exposure (see Table  S2  for parameters used).
Principal component analyses (PCA) were performed as a first step to examine QC clustering, sample separation and identify any analytical or biological outliers (See Fig. S2  and S3 ). Sample groups were then defined as follows in order to perform Sparse Partial Least Squares Discriminant Analyses (sPLS-DA) models: baseline group = samples collected two days and just prior to intake of APAP, 1-6 h group = samples collected + 1, +2, +4, +6 h after intake and +1-2 days group = samples collected in the morning the subsequent two days. sPLS-DA was performed with mixOmics R package (version 6. 8. 5) in the mode regression  (Rohart et al. , 2017 ) (See Fig. S4  and S5 ). Besides multivariate analyses, univariate analyses were performed to compare fold changes of markers between the three different groups with the mixOmics R package.
A corresponding p-value was calculated with the multtest R package using Unpaired Student's t-tests, with an Adaptive Benjamini and Hochberg (ABH) correction for multiple comparisons to identify markers discriminations between groups. All markers having pvalue < 0. 01 were selected for further tentative annotation process. Discriminating markers between the three groups identified through multi-and univariate analyses were first screened using a list of known APAP metabolites. The suspect list was build using information already available in the literature and using in silico software tools that predicts small molecule metabolism in mammals (e. g. , BioTransformer  (Djoumbou-Feunang et al. , 2019a) ). Identities of these expected APAP metabolites were determined from accurate mass, isotopic fit, fragmentation data obtained from SWATH acquisition and from comparison with standard compounds when available or spectra available in online libraries or the literature.
In order to provide confidence in metabolite identification, we supply confidence levels based on recommendations made by  Schymanski et al. (2014)  (see Supp. Table  S6 ). The structural identities of unknown discriminating markers were determined from their accurate mass, isotopic fit, fragmentation data obtained from SWATH acquisition. These parameters were compared with those from online libraries such as PubChem  (Kim et al. , 2019) , ChemSpider  (Pence and Williams, 2010) , KEGG  (Kanehisa, 2002) , HMDB  (Wishart et al. , 2018b) , Metlin  (Guijas et al. , 2018) , T3DB  (Wishart et al. , 2015) , MassBank  (Horai et al. , 2010)  or DrugBank  (Wishart et al. , 2018a). When no experimental spectra were available, MS/MS fragmentation patterns of individual markers were collected and annotation was based on online software such as MetFrag  (Ruttkies et al.
, 2016)  and CFM-ID  (Djoumbou-Feunang et al. , 2019b) ) which allow to perform in silico fragmentation of candidate molecules from different databases (e. g. , PubChem) and search for matched against mass to charge values. When possible, chemical identity was confirmed from accurate mass, isotopic fit and fragmentation data obtained from standard compounds (highest level of confidence). In the end, only annotations with a confidence level higher than 2b (i. e. , based on MS/MS match with experimental or in silico MS/MS spectra) were reported. 2. 1. 4. 6. Accurate integration of identified and annotated markers. Independent peak integration and normalization were carried out to validate the significance of the discriminated metabolites between groups. All the markers identified were manually re-integrated using the Sciex OS Analytics tool.
170 mg of luminol powder was dissolved in 15 ml of 3. 75 mM NaOH solution that was diluted with 1 L of DI water to achieve a solution with pH = 11. 73. A signal processing is needed to get a quantitative analysis of the number of photons that might be emitted. We are expecting for random events, that are not subject to produce a DC voltage but rather a series of discrete pulses  [18]. One must be able to discriminate between the electronic noise of the PMT and the luminescent signal, and to record the signal over a duration providing a convergence of the rate of events, for a fixed flowrate. A MATLAB code has been developped, tested, and validated with periodic referenced signals (square waves at 100 Hz and 1 kHz) at different sampling rates (50 kHz, 5 MHz and 50 MHz). The code provides the right values of the frequencies and of the area of the recorded voltage integrated over time.
Results and discussion Experiments were performed at ambient temperature. The hydrodynamic characterizations of the microdevices MSD and MDD have been first measured with deionized water as the working fluid. The total pressure drop is mainly monitored by the constriction inside the channel, and obeys : ∆P = K. ½. ρu 2 (2) where ρ is the density of water (ρ = 1000 kg/m 3 ), u is the average velocity (m/s) through the constriction. One finds experimentally K = 1. 67 and K = 1. 76 for the microstep and the microdiaphragm devices respectively. For MSD, the inception of cavitation arises above the critical flow rate Qcav = 3. (1). The data are displayed in Figure  3. The number of events raises as a power function of the flow rate Q m with 5 < m < 6. The influence of the nature of dissolved gases has also been confirmed (Figure  4 ).
Luminescence from deionized water occurs as soon as the cavitating flow regime is activated (Figure  8 ). Similarly, the chemiluminescence of the luminol solution cavitating through the microdiaphragm device MDD is clearly noticeable, the number of events being sixty times that of luminescence of DI water. It is possible to conduct a comparative study between hydrochemiluminescence of alkaline luminol solutions and hydroluminescence of water, because corresponding data were recorded under similar cavitating flow regimes. Data from both devices under test have been gathered and plotted in Figure  9 , as a function of the relative flow rate Q/Qcav. The evolution of the ratio CL/L between the number of photons emitted by chemiluminescence (CL) and the number of photons emitted by luminescence (L) does not depend on the uncertainties on backscattered photons, quantum efficiency of the PMT and of the chemiluminescence of luminol.
Acknowledgments The author acknowledge the financial support from the  Centre National de la Recherche Scientifique  (C. N. R. S. ) via the program 'green engineering' of the  INSIS Department. They also acknowledge the  TEC XXI  fundation which has provided the post doctoral fellowship of one of us (L. P. ). Deep reactive ion etching was kindly performed by  A. Barbier  and  Dr E. Griessen  at  IRAM, Grenoble. Other micromachining steps were performed thanks to the Nanofab facilities at the  Neel Institute of Grenoble. Helpful technical assistance was furnished by  V. Govart  from  LEGI. The authors acknowledge  Dr G. Ledoux  for useful discussions. L. P. built the experimental set up, developed the Matlab code, performed the experiments, analysed the data. D. C. has micromachined the samples and designed the hydraulic circuitry, developed the Matlab code, contributed to the implementation of the experimental set up.