dataesr/scientific-paragraphs-categorization huggingface dataset:
The entire Eurofordocs database (with all beneficiaries, without time limitation) contains 5. 5 million contractual agreements, 3. 3 million of which have a nil amount. 2. 2 million gifts are reported to be linked to an agreement but have an invalid textual link. There are, therefore, at least 1. 1 million contractual agreements with a nil amount despite the legal obligation to declare them. Another limitation lies in the fact that the data comes from the declarations of the pharmaceutical industry itself with typos. Moreover, there may be a delay in data reporting, and remunerations may have been misclassified as it was possible to declare them as gifts or as remunerations until October 2017. Finally, in the absence of an official definition, we chose an objective but restrictive definition of a KOL which led us to rule out many individuals with great leverage who could also have been included as KOLs.
Comparison with other studies Our results are in line with those observed worldwide but add new data regarding the French context. A very recent US study showed that nearly three-quarters of the leaders of the 10 most influential professional medical associations in the USA had ties with the pharmaceutical industry, with wide variations in the amount of payments reported between the professional medical associations. 11 otal general payments of US$24. 8M (€20. 8M, £18. 9M) were linked to the 235 KOLs of the 10 most influential professional medical associations over 3 years. The total median general payment was US$6000 (IQR US$309-US$54 000) (€5000, £4500). In the American study, KOLs received 10 times more per capita per year in total amount than the French KOLs, and more than 83 times more in terms of median amount.
Presents the three categories of financial ties and the date from which they had to be declared in the transparency in healthcare database(Transparence-Santé)Identity of the parties concerned, the organiser, the name, date and place of the event, date of the agreement, its precise purpose (mandatory since the law of 2013; actual availability on the website since 2014) and the amount (mandatory since the law of 2016, actual availability on the website since 2017). If the agreements give rise to payments in gifts or remuneration, the payments can be reported in the agreements category or the gifts or remunerations categories, with a numerical link to the agreement. Identity of the parties, final beneficiary, date of payment, amount if it is greater than or equal to €10 (available since 2015 but mandatory since the law of 2016, actual website availability in the remuneration section in 2017).
Acknowledgements The authors thank  Pierre-Alain Jachiet  for his proofreading of the manuscript, his help to fully understand the database and properly use EurosForDocs. Patients and public were involved through the French FORMINDEP association that aims to improve the independence of physicians' medical education. FORMINDEP members (patients and physicians) kindly accepted to participate by reviewing and editing the manuscript. The French CI3P organisation (Patient and  Public Partnership Innovation Centre of the Faculty of Medicine of Nice ) also accepted to participate in the manuscript's revision and editing. Their comments improved the manuscript's quality, especially the discussion. The authors also thank the reviewers and the editor for valuable comments that helped to improve the manuscript and  Yvonne van der Does  ( Office of International Scientific Visibility, Université Côte d'Azur ) for proofreading.
Data availability statement Data may be obtained from a third party and are not publicly available. The Transparency in Healthcare database was downloaded on 18 May 2020 from the website EurosForDocs. Data from EurosForDocs are available on https://www. eurosfordocs. fr/data#donn-es. Analytic code from the study is available on https://osf. io/4756p/?view_only=df16d649d87847e5aa94 78960620bf81.
The transparency in healthcare database was laid down in the 'strengthening the safety of medicines and health products' law of December 2011, and launched in July 2014. onMay 6, 2022  by guest. Protected by copyright. http://bmjopen. bmj. com/ BMJ Open: first published as 10. 1136/bmjopen-2021-051042 on 2 February 2022. Downloaded from.
In 2012, researchers showed that the chromatographic system influenced the mass accuracy of untargeted HRMS. In particular, they fund that high chromatographic separation power is essential to obtain high-quality HRMS data, as the mass accuracy and thus the number of detected compounds are strongly influenced by the co-eluting compounds  [9]. This also demonstrated the need to optimize HRMS-based detection methods, especially when the results are for use in clinical or forensic settings. In this context, new powerful bioinformatics tools are also emerging such as molecular networks or metabolomics, since the optimization steps include the reprocessing of the acquired data  [10, 11]. Despite their great diversity, more and more databases used in HRMS for toxicological screening, listing NPS in particular, are emerging and allow faster detection, efficient medical management and retrospective data processing.
The list of possible candidates can be narrowed down to one or a limited number of compounds, which can be further investigated in the available databases. In other words, the analysis can be performed initially without reference standards, because the overall information provided by HRMS (full-spectrum acquisition, precise mass of the (de)protonated molecule and relevant fragment ions, and isotopic profile) is relevant and allows the putative identification of the detected compounds  [15]. The ability to screen without having standards a priori is very useful, as laboratories do not need to purchase all the reference standards prior to analysis, with the attendant problems of cost and expiration dates. Reference standards are needed for final confirmation, but only as a last step, when there is strong, well-founded evidence that the compound has been identified in the sample.
Solid-phase extraction (SPE) was used as sample preparation prior to ultra-high performance liquid chromatography and TOF-MS using fragmentation without precursor selection (all-ion MS/MS) to avoid loss of information. Application data were included for 50 authentic urine samples. However, particularly extensive sample preparation, such as SPE and liquid-liquid extraction (LLE), could be replaced by simple dilution or precipitation using these highly sensitive systems  [23, 24]. This approach is also contributive in judicial seizures, allowing the rapid identification of products and subsequent legal sanctions. Furthermore, the detection of impurities and cutting products is easier, sometimes revealing a ''fingerprint'' that allows the location of the manufacturers to be traced. These techniques also have limitations such as the cost of instruments, the complexity of data processing software and the requirement of skilled analysts  [25].
Query online compound databases and/or perform structure elucidation In untargeted screening for NPS analysis, peak selection and filtering are performed on the full HRMS data to select a smaller number of peaks that may provide a clue about an NPS. In the untargeted approach, peak searching involves extracting all detectable peaks in a single analysis using a peak selection algorithm to generate a list of candidate masses that can be reliably used for identification. This shortlist of candidate masses can then be compared to existing MS libraries and/or used for mass spectral structure elucidation. The peak selection algorithm uses raw full-scan MS data (without prior knowledge of specific ions) to search for peaks that have a common elution profile. The masses are then grouped into compound masses, resulting in a list of candidate masses.
Acquire/purchase curated spectral databases for NPS Purchasing a mass spectra database for NPS screening ensures that the screening data are accurate, collected systematically, and often accompanied by recommended LC and MS acquisition parameters and workflows for use in the intended contexts. This screening strategy does not rely on free online tools, but it is a very feasible alternative. HRMS screening libraries can be purchased, such as the ''LC-HRMS/MS Library of Drugs, Poisons, and Their Metabolites''  [50] , the ''Mass Spectra of Designer Drugs''  [51] , or the ''Wiley Registry of Tandem Mass Spectral Data, MS for ID'' [52]. Waters ® , Thermo Fisher ® , Agilent ® and SCIEX ® also provide spectral libraries tailored to their respective instruments.
Download crowd-sourced screening library MS data from one instrument can be used as reference data on another MS device with comparable parameters for screening purposes  [53]. For this reason, several users can share and use screening data. These data sources include data repositories such as the NPS Data Center  [54] , the RESPONSE database  [55]  or HRMS databases such as Massbank  [56] , MzCloud  [57]  or HighResNPS  [58]  (Table  2 ). The HighResNPS. com homepage was designed as a repository of HRMS data for NPS detection that can easily be converted into a screening library. The primary molecular identifier on HighResNPS is the InChIKey, which provides an unambiguous identifier, as opposed to a given name, SMILES string, or IUPAC name. Each data entry must include the protonated molecular ion and optionally one to three diagnostic fragment ions.
Each user adds information about the analytical setup they use to generate their results. If some users have identical analytical setups, they can also use retention times with the MS data, which increases confidence in detection. HRMS identification of unknowns using HighResNPS does not distinguish between positional isomers, which could account for most of the incorrectly assigned NPS. In addition, High-ResNPS. com has been used successfully to identify NPS in seizures  [59] , environmental samples  [60] , and for the retrospective analysis of post-mortem biological samples  [61]. In the current setup, data quality in HighResNPS increases when multiple users add data on the same compound. Unlike the NPS datahub, user-entered data are not manually verified and classified. Diagnostic fragment ions are added to the database with the molecular formula and exact mass, reducing the risk of rounding errors or mistakenly adding exact masses.
Two limitations of HighResNPS. com are the manual data curation and the lack of peer reviewing. Continuous curation is an advantage of the commercial databases, which have designated funds and labor for this. When a given NPS has multiple analytical entries in the database, the data selfvalidate, since different users with different standards are unlikely to report the same faulty data. Thus, if a positive hit is obtained after screening with HighResNPS, the user can further support identification by retrieving full HRMS spectra on MzCloud and/or additional information on NPS Data Hub, and ultimately obtain an unambiguous identification with an analytical reference standard. Note that when identification is based on HighResNPS screening data, it must always be confirmed using additional identification techniques, multiple analytical targets and/or analytical reference material.
Cell Cycle Analysis Cell concentrations of SdFFF-sorted subpopulations were standardized in each condition. After a centrifugation step, the cells were resuspended with cold PBS, fixed with cold 96% ethanol added slowly and under shaking, and then placed at -20 • C. After a few minutes at room temperature, the cells were washed and then resuspended with PBS and RNAse A (no. R6148, Sigma-Aldrich, Saint-Louis, MO, USA) fo) for 20 min at room temperature. Next, propidium iodide was added 15 min before acquisition on the FACS Calibur. Data analysis was performed with ModFit LT TM software v5. 0 (Verity Software House, Topsham, ME, USA).
Four weeks later, the formed colonies were fixed in 4% PFA (no. 10231622, Thermo Fisher) for 15 min and then stained with 0. 1% crystal violet. Wells of the 24-well plates used were captured with the Leica DFC300 FX Digital Color Camera to allow colony quantification and analyzed by ImageJ software v1. 53e (National Institutes of Health, Bethesda, MD, USA).
In Vivo Tumor Initiation Assay SdFFF-sorted cell subpopulations of the WiDr cell line were injected subcutaneously into nude mice (Hsd:Athymic Nude-Foxn1nu nu/nu, six weeks old, female, five mice per group) in decreasing amounts of cells (1000, 500, and 100) in Matrigel (no. 356237, Corning, New York, NY, USA)-MEM medium. Mice weight and tumor size were measured three times a week for 7 weeks. After 50 days, mice were sacrificed and tumors were collected. The number of tumor-bearing mice with a tumor volume greater than 100 mm 3 was counted. The online software Extreme limiting dilution analysis (ELDA) was used from the in vivo results to determine the frequency of tumor occurrence and thus tumor initiating cells frequency (https://bioinf. wehi. edu. au/software/elda/, accessed on 28 January 2022)  [16].
Cytotoxicity Assay To compare results between cell lines, we defined the same cell concentration for all cell lines: 1. 5 × 10 3 cells per well of 96-well plate, after optimization. Once sorted and seeded, cells were then treated for 72 h with 5-FU using a range from 0. 16 to 250 µM. Three days later, the MTS reagent was added according to the manufacturer's instructions (no. G3580, CellTiter96 AQueous One solution Cell Poliferation assay, Promega, Madison, WI, USA) and incubated for 3 h at 37 • C under a humidified atmosphere of 5% CO 2. Absorbance was then measured at 490 nm with the Multiskan ™ FC 96-well plate reader (Thermo Fisher) and the results were expressed as a percentage comparing the treated condition to the untreated condition defined as 100%. Generation of drug response curves and determination of IC50 values were performed using GraphPad Prism software (San Diego, CA, USA).
Then, to study chemotherapy response in a 3D culture model, chemotherapies were added after the seven days of incubation that allow colonosphere formation, and incubated for three days. Chemotherapy doses used in the 3D chemosensitivity assays were defined from the results of the 2D cytotoxicity assays that allowed calculation of the average IC50 for all cell matrices, including cell lines and primary cultures. Colonospheres were treated using the average IC50 obtained (0. 7 µM for 5-FU, 1. 9 µM for oxaliplatin and 22. 8 µM for irinotecan) for both single and combination therapy. Colonospheres were counted three days after treatment, imaged using the Leica DMi8 microscope, and their size was measured using ImageJ software v1. 53e (National Institutes of Health).
Results were analyzed by the Kruskal-Wallis test for clonogenicity assay because the results did not follow a normal distribution, verified using the Shapiro-Wilk test. Chi-squared test was used to identify the frequency of tumor-initiating cells from in vivo experiment results. The one-way ANOVA test was used for all other experiments because the results followed a normal distribution (Shapiro-Wilk test) and the comparison was between the three sorted cell subpopulations and the control. Statistical tests were performed using PAST software, version 2. 17c.
These results suggest that tumors obtained after injection of the F3 subpopulation are more aggressive than those obtained from F1 cells. The size of the tumors can be visualized from the photos of the tumors collected at the end of the experiment in Figure  S1E. Based on these in vivo results, we performed a limiting dilution analysis (LDA) using the extreme LDA software  [16]. The frequency of tumor-initiating cells was estimated at one cell in 566 for the F3 subpopulation, compared with one in 3611 for F1. Thus, the frequency was four-fold higher for the F3 subpopulation compared to F1, with a significant p-value (p = 0. 00886) (Figure  3C ). Therefore, the F3 subpopulation is more tumorigenic than F1 and has the particularity to form tumors even after an injection of only 100 cells. Furthermore, F3 cells have a significantly higher frequency of tumor-initiating cells compared to F1, confirming our in vitro results and proving that the F3 subpopulation is enriched in CSCs.
Acknowledgments: We would like to thank the anticancer preparation unit of the  Limoges University Hospital  for the chemotherapies, especially  Gaëlle Maillan ; the animal facility of the BISCEM platform for their help during the in vivo experimentation;  Alice Hémard  for her help during her internship; and the anatomo-pathology department of the  Limoges University Hospital , in particular  Sandrine Robert ,  Alain Chaunavel , and  Aurélie Charissoux. We would also like to thank  Audrey Ferrand ,  Céline Gongora , and  Corinne Bousquet  for the relevant discussions during  Céline Hervieu 's thesis defense.
Acknowledgements The authors would like to thank  Mélanie Legros  and  Marc Schmutz  for the access to ICS characterization and electron microscopy platforms respectively. Drenckhan Wiebke  and  Jacomine Leandro  are acknowledged for their support during the surface tension measurements while  Jean Muller  is thanked for his help in processing the NPs' size data. Special thanks are addressed to  Campus France  and  French Embassy in.
, Altrincham, UK), whereas approximately 50 µL of spore suspension of A. brasiliensis was spread over the surface of Czapek Dox agar media. Then, four wells (0. 7 mm in diameter) were prepared in each plate before being filled with 100 µL of AuNP solutions (200, 100, 50, 25, 12. 5, 6. 25, and 3. 12 µg mL -1 ). The DMSO (solvent system) was systematically used as a control. The loaded plates were incubated at 35 ± 2 • C for 24 h for bacteria and C. albicans and at 25 ± 2 • C for 72 h for A. brasiliensis. The results were recorded as the diameter of clear zones (mm) formed around each well after the selected incubation period. The minimum inhibitory concentration (MIC) for each organism was detected as the lowest concentration of synthesized AuNPs that formed a clear zone. The experiments were performed in triplicate, and the data are represented as average values and standard deviation intervals.
Cell viability (%) = Absorbance of treated sample Absorbance of control × 100 (3) The IC 50 values were calculated using curve fitting (cubic spline) with Prism software (GraphPad Software, San Diego, CA, USA) by an algorithm described by Chambers et al. [46]. Results were statistically analyzed using SPSS v18 (SPSS Inc. , Chicago, IL, USA). An analysis of variance (ANOVA) test was used for multiple sample comparison, followed by Tukey's multiple comparison test.
Crystallinity Characterization The crystalline structure of phytosynthesized AuNPs was investigated by X-ray diffraction (XRD). As seen in Figure  3A , the XRD pattern shows four intense reflection peaks at 2θ degrees in the range of 5-80 •. These peaks are identified at 2θ values of 38. 2 • , 44. 4 • , 64. 7 • , and 77. 7 • , which correspond to standard Bragg diffractions of the (  111 ), (  200 ), (220), and (311) planes, respectively. These diffraction peaks correspond to the face-center crystal lattice  [40]. The obtained data are compatible with those reported for the green synthesis of AuNPs using plant extracts  [56, 61, 62]. The obtained XRD pattern also confirms the crystalline nature of AuNPs according to the standard file of JCPDS No. 04-0784  [26]. The absence of extra diffraction peaks indicates the high purity of plant-based AuNPs.
Consistently with this study, AgNPs synthesized by an aqueous extract of Acanthospermum australe exhibited antimicrobial activity toward various skin and soft tissue infection microbes with low cytotoxic activity against human normal cells  [89]. Mussin et al. reported that this high antimicrobial activity is due to AgNPs rather than to the plant metabolites that coat the NP surfaces. The effectiveness of infection control depends on the treatment strategy chosen; this also affects the evaluation of MIC (minimum inhibitory concentration) values  [90]. In the current study, MIC values were evaluated based for the lowest concentration of AuNPs to inhibit microbial growth. Data analysis revealed that the MIC value was 25 µg mL -1 for B. subtilis and A. brasiliensis, with ZOIs of 11. 3 ± 0. 6 and 10.
Analysis of variance clearly shows that the scavenging activity decreases with reduced concentration. For instance, the scavenging activity percentages were recorded at values of 82. 5 ± 1. 4, 77. 2 ± 2. 2, 62. 7 ± 2. 9, and 31. 8 ± 3. 1% at concentrations of 500, 250, 62. 5, and 1. 9 µ g mL -1 respectively, as compared to a control, with percentages of 94. 1 ± 0. 2, 87. 8 ± 0. 1, 75. 1 ± 0. 2, and 37. 5 ± 1. 3% at the same concentration for ascorbic acid. The IC50 values for phytosynthesized AuNPs that correspond to a scavenging efficiency of 50% were calculated. Data analysis shows that the IC50 value for AuNPs was close to 16 µ g mL -1 , compared with a value of 12. 8 µ g mL -1 for ascorbic acid. In agreement with our results, Sargassum longifolium-mediated AuNPs displayed considerable antioxidant activity against H2O2 (25 µ g mL -1 ) and DPPH free radicals (20-30 µ g mL -1 )  [44].
Acknowledgments: The authors extend their appreciation to the  Botany and Microbiology Department, Faculty of Science, Al-Azhar University, Cairo, Egypt , for the great support in the achievement and publication of this research work. Furthermore, the authors acknowledge the support from  Princess Nourah bint Abdulrahman University Researchers Supporting  (project number  PNURSP2022R15 ),  Princess Nourah bint Abdulrahman University, Riyadh ,  Saudi Arabia.
5 ppm; 13 C, 39. 5 ppm). Chemical shifts (δ) are reported in part per million (ppm) and coupling constants (J) are reported in Hz. Data are presented as follows: chemical shift, multiplicity (s = singlet, d = doublet, t = triplet, q = quartet, m = multiplet, br = broad), integration and coupling constant (J in Hz). Assignments were determined either on the basis of unambiguous chemical shifts or coupling patterns. DEPT-135, COSY, HSQC or HMBC experiments were performed when necessary to complete spectral attribution for related compounds. 13 )]pentan-3-one 3. A solution of BF3. OEt2 (14 mL, 108 mmol, 6 eq. ) in anhydrous CH2Cl2 (12 mL) was added dropwise over 15 min to triethylorthoformate (13. 35 g, 15 mL, 90 mmol, 4. 5 eq. ) at -30 °C. The reaction mixture was warmed up to 0°C, and stirred for 20 min to give slurry of tetraethoxycarbenium fluoroborate.
In France, the abuse/misuse of psychoactive substances, including cocaine, is monitored via spontaneous notifications, and under-reporting is its main limitation. Therefore, the French national hospital discharge database (Programme de Médicalisation des Systèmes d'information [PMSI]) was used to identify all hospital stays possibly due to complications related to cocaine use. The objective was to determine the main trends in the rate of cocainerelated hospitalizations from 2010 to 2019 by age category and by areas. Relevant PMSI data were extracted using the International Classification of Diseases (10th edition). In France, hospitalizations for cocaine-related complications increased by fourfold (2461 in 2010, 9843 in 2019, +300%). This increase was similar in men and women and was observed in each age category.
The spontaneous reporting system remains a useful tool to generate "signals" or "warnings" of severe drug-associated events. However, under-reporting is considered its main limitation. The low notification rate is one of the reasons for the establishment of pharmacoepidemiological tools by the FAN  [8]. To complete the FAN data, some groups have analyzed hospital records to identify serious complications related to the use of psychoactive substances  [8] [9] [10]. To obtain information on 10-year trends in cocaine-related hospitalizations, we used the French national hospital discharge database (Programme de Médicalisation des Systèmes d'information [PMSI]) taking into account categories of age and French administrative regions to identify possible changes in hospitalizations related to cocaine use.