dataesr/scientific-paragraphs-categorization huggingface dataset:
(iii) Data from the French epidemiological OSIAP survey. This national program continuously collects and analyses the forged or falsified prescriptions presented by patients in community pharmacies and ranks the most frequently identified drugs on falsified prescriptions. French pharmacists are not mandatory to declare to the FAN the falsified prescriptions that they detect: OSIAP relies on spontaneous declarations  (Jouanjus et al. , 2018). For this study, we analysed the positioning of zopiclone among the other most frequently cited drugs.
Statistical Analysis The evolution of zopiclone volume and importance was presented per year from 2014 to 2020. A description of zopiclone NOTS characteristics was performed for the 2 study periods for the total population and for each period according to the status misuser/nonmisuser. Quantitative and qualitative analysis of notification characteristics used continuous data expressed as the mean and categorical data expressed as numbers and percentages. Comparisons were used to assess evolution over time, using chi-squared test for proportions and Student's t-test for means. Analyses were carried out using R version 4. 2 Software and statistical significance was fixed at p < 0. 05.
Ethical Consideration In France, health professionals have a legal obligation, mentioned in the Public Health Code, to report observed cases of abuse or dependence involving a psychoactive substance to the FAN. This reporting obligation is a French specificity which makes the French addictovigilance database unique and allows the collection of a large amount of data from health professionals. These data are collected by the FAN and recorded anonymously in the national database. FAN pharmacologists are commissioned by the health authorities to analyse these cases of abuse and dependence. In accordance with the regulations in force, no further authorization is therefore needed.
Acknowledgements We wish to thank the  French Addictovigilance Network (FAN)  for making this work possible:  Anne-Sylvie Caous ,  Samira Djezzar ,  Alexandra Boucher ,  Amélie Daveluy ,  Céline Eiden ,  Bernard Fauconneau ,  Christine Fournier-Choma ,  Valérie Gibaja ,  Clémence Lacroix ,  Maryse Lapeyre-Mestre ,  Reynald Le Boisselier ,  Bruno Revol.
Data Availability In France, health professionals have a legal obligation, mentioned in the Public Health Code, to report observed cases of abuse or dependence involving a psychoactive substance to the French Addictovigilance Network. These data are collected by the FAN and recorded anonymously in the national database of ANSM (French National Agency for the Safety of Medicines). Data sources: French National Agency for Medicines and Health Product Safety (ANSM). The content of this article is the sole responsibility of the authors and has not been validated by the ANSM. Code Availability Code can be transmitted by the corresponding author upon reasonable request.
All participants provided written informed consent, and the METACARDIS (Metagenomics in Cardiometabolic Diseases; NCT02059538) study was conducted in accordance with the Helsinki Declaration. The Ethics Committee of each participating country approved the clinical investigation. A detailed list of prescribed medications, anthropometric data, clinical history, and a fasting blood sample were obtained at enrollment. N o r t h A m e r i c a n c o h o r t. Patient samples and clinical data used for the independent validation cohort from North America were obtained from the GATC (GeneBank at the Cleveland Clinic: Molecular De-. GeneBank is a single-site sample repository generated from consecutive patients undergoing elective diagnostic coronary angiography or elective cardiac computed tomographic angiography with extensive clinical and laboratory characterization and longitudinal follow-up.
FUNDING SUPPORT AND AUTHOR DISCLOSURES This study was supported by the  Transatlantic Networks of Excellence Award  from the  Leducq Foundation  ( 17CVD01 ),  FP7-sponsored program  MetaCardis (305312),  JPI  (A healthy diet for a healthy life;  2017-01996_3 ),  Swedish Heart Lung Foundation  ( 20210366 ),  Swedish Research Council  ( 2019-01599 ), AFA insurances (160337),  Knut and Alice Wallenberg Foundation  ( 2017. 0026 ), the  Novo Nordisk Foun-.
Manual revision was performed to correct errors and improve translation accuracy. The resulting DiAna dictionary was linked to the ATC classification, proposing a primary ATC code for each ingredient. Results: We retrieved 18,151,842 reports, with 74,143,411 drug entries. We automatically translated and manually checked the first 14,832 terms, up to terms occurring at least 200 times (96. 88% of total drug entries), to 6,282 unique active ingredients. Automatic unchecked translations extend the standardization to 346,854 terms (98. 94%). After linking to the ATC classification, the most prominent drug classes in FAERS reports were immunomodulating (37. 40%) and nervous system drugs (29. 19%).
The need for transparency on data preprocessing Spontaneous reporting systems (SRSs) are public and private services collecting reports of suspected adverse drug reactions in order to timely detect potential issues in drug safety  [1, 2]. However, these reports are gathered from a variety of regional, national, and manufacturer databases, which have different languages, rules, and forms for data storage. Furthermore, the same SRS may collect reports from different kinds of reporter (e. g. , manufacturers, healthcare professionals, lawyers, consumers) and using both paper and electronic forms. This heterogeneity leads to highly variable content, particularly in free text fields which may contain misspellings, out-of-context information, and the use of different lexicons. Furthermore, reports may be incomplete, due to the spontaneous nature of the reporting, or duplicates, due to multiple reporting and system errors.
For these reasons, an extensive cleaning procedure is needed prior to any kind of statistical analysis. The United States (US) Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) is one of the most used SRS in signal detection, because of its free access and its large catchment area mirroring the entire world (even if with a large representativity for the US). There are two ways the FDA provides free access to the FAERS data, with notable differences. First, already pre-processed data through an online public dashboard [3], developed for transparency reasons and the promotion of higher quality reporting. However, this tool is obtained from spontaneous reports through a cleaning procedure that is, allegedly, both undisclosed and partial, for example because it does not attempt any duplicates detection and it does not provide all the information reported in individual case safety reports (ICSRs).
Therefore, it is not suitable for research purpose, namely complex analyses beyond simple exploratory analyses. Second, raw quarterly data (both in ASCII and XML format)  [4]  that allow to knowingly perform and document the entire pre-processing procedure. This cleaning and normalization procedure requires the researchers a conspicuous effort and faces them with the need to make multiple operative choices: for example how to deal with duplicates, how to deal with dates that up to 2012 were completed automatically when partial, how to deal with unclear entries (e. g. , in 2019 "RN" was often recorded as a reporter type; while this entry may refer to registered nurses, it is not documented in the "readme" file of the FAERS). These choices are seldom driven by objectivity alone and must be considered both in the design and the interpretation, because the same database cleaned with different procedures may give different results  [5].
The inconsistencies that derive from the multiple operative options and researcher's personal choices can affect case-retrieval and impair replicability among studies. Nonetheless, no consensus on the best operative procedures has been achieved, and already-published analyses using SRSs' data are rarely transparent on the cleaning choices adopted, lacking documentation on whether and how the FAERS was prepared for statistical analyses. The FAERS system itself, which does have a formal dictionary through which it cleans the data for the public dashboard, does not make it publicly available. Among the 17 studies conducted using the FAERS quarterly data and published in February 2023 (accessed on PubMed on March 15 th , 2023), ten did not state any drug standardization process, six used an automatic translation via dictionaries, one performed a manual translation but did not make it publicly available (Table  S1 ).
This lack of transparency also affects some free readyto-use pharmacovigilance databases which provide already pre-processed FAERS data  [9, 10]. Moreover, these databases standardize drugs only according to US dictionaries of drug names (e. g. , RX-norm, orange book), thus failing to identify foreign drug names, misspellings, and other free text issues that were described above. The WHODrug Global is a dictionary that compiles extensive drug information from across the globe  [11]. However, it does not consider the challenges posed by free text issues, and it is not available as an opensource resource. Other tools attempt to edit drug names using an automatic translation of potential misspellings, which may result in mistranslations due to the existence of similar drug names referring to formulations with different active ingredients  [12].
The FAERS database We downloaded FAERS Quarterly Data (trimestral) Extract Files  [4]  in ASCII format from 04Q1 to 22Q4. These files are composed of five tables linked through a primary key ("primaryid") identifying a specific version of a report: DEMO (demographic and administrative information), DRUG (information on reported medications), REAC (adverse events), OUTC (outcomes), RPSR (report sources). DRUG is also linked through "primaryid" and a secondary key ("drug_seq"), identifying a specific medication within a report, to other two tables: THER (start dates and end dates for the reported medications), INDI (indications for using the reported medications).
Automatic set up of the dictionary We combined all DRUG quarters into one database. We focused on three columns used to identify the medicinal product: • Drugname, recording the name of the medicinal product. • Prod_ai, recording the product's active ingredients, when available. • Val_vbm, recording whether the source of drugname was a validated trade name (value = 1) or a verbatim name (value = 2). Since our aim was the translation to active ingredients, we did not consider the column "val_vbm". We instead retrieved all the unique terms from the other two columns (i. e. , Prod_ai and Drugname), lowered upper cases, and removed multiple spaces, leading and trailing spaces and punctuation, and spaces between parentheses and included text. We merged the unique terms with RX-norm 7 and WHO-ATC substances 8 to create a dictionary with automatic translations to active ingredients.
Manual revision We then manually revised all the automatic translations starting with the most frequently reported ones up to the terms recorded in a minimum of 200 reports (and beyond, ongoing). We started translating drug names to active ingredients included in the 2023 update of the ATC classification of the WHO, integrating it whenever we met new active ingredients. Hand search of foreign drug names was performed using online databases (e. g. , DrugBank. com  [14]  and Drugs. com  [15] ), manufacturer websites, and websites storing information from foreign package labels (e. g. , Kusuri-no-Shiori -drug information sheets-from the Japanese regulatory agency, accessed at https://www. radar. or. jp/siori/english/).
Linkage to the ATC classification Furthermore, we performed data-linkage between the DiAna dictionary and the hierarchical ATC classification, which was downloaded from the WHO Collaborating Centre for Drug Statistics and Methodology website [16] using the R package "rvest". Since this classification is mainly a tool for drug utilization research, the same active ingredient may be given more than one ATC code if it is available in multiple strengths or routes of administration with clearly different therapeutic uses  [17]. We linked the final list of individual active ingredients from our translation with the ATC classification, manually integrating for different choices in the nomenclature (e. g. , "vitamin b9"-folic acid-to B03BB01), for classes of drugs (e. g. , "antihypertensives, unspecified" to C02), and for drugs recorded in the ATC only in combination (glecaprevir and pibrentasvir both to J05AP57).
Results We downloaded the FAERS quarterly data up to 22Q4 and retrieved 18,151,842 ICSRs, for a total of 74,143,411 drug entries (92. 81% allegedly recorded using a validated trade name) and 955,778 unique drugname and prod_ai terms (see Figure  1 ). After the initial formatting, we reduced them to 793,274 unique entries. We automatically translated 346,854 terms (98. 94% of total drug entries) and manually checked the first 14,832 terms (96. 88%) up to 174 occurrences (<0. 00015%, ongoing). A total of 6,282 unique ingredients were included in the DiAna dictionary, of which The translation also took account information about placebo and experiments, thus identifying 50,967 reports as generated within trials (0. 28%).
The DiAna Dictionary The sensitivity of case retrieval and the relevant disproportionality analysis results may vary depending on the drug cleaning procedures used in SRSs. Disproportionality analysis is mostly performed on public dashboards or other analytical tools with no access to To address these concerns, a dictionary for drug name-to-ingredient translation was developed through an automatic procedure that was manually checked and extended. This dictionary, called DiAna dictionary, required a time-consuming effort and is made available opensource for everyone to use it and propose changes. The use of the DiAna dictionary will allow the pharmacovigilance community to agree on the best possible translation. A greater control on data cleaning will result in improved replicability and accuracy of signals, and more conscious and appropriate interpretation of results, with relevant benefit for the scientific community.
We have also decided not to translate to salts as this is rarely taken into account in disproportionality analysis and can lead to confusion about whether the same ingredient with unspecified salt should be considered among cases or non-cases. Instead, we have included the linkage to the ATC classification, and provided translation also to higher ATC classes such as "antihypertensives, unspecified", as this information can be important for adjusting the analysis and assessing individual cases. The DiAna dictionary translates a higher proportion of the database, enabling a higher sensitivity in case retrieval, and a higher number of identified cases. This results in better specificity in the definition of non-cases and higher accuracy in signal detection. This means earlier and clearer signals, as in some specific products the number of reports retrieved significantly increased.
For example, for rimegepant the DiAna dictionary identifies 278 times more reports than RxNorm alone. In addition to identifying active ingredients, the drug name information enabled us to identify reports derived from clinical trials (0. 28% of total reports), as they recorded placebo, blinding, or drug codes. This information can help researchers exclude evidence already taken into account in other steps of drug safety characterization from the disproportionality analysis. Finally, the linkage between the DiAna dictionary and the ATC classification can help in the retrieval of drug classes and in visualization. The information on the distribution of drug classes in the database is particularly useful for the design of future disproportionality analyses, as it provides insight into the representativeness of the population chosen as comparison.
Over one-third of the database consists of reports documenting the utilisation of immunomodulating drugs. Recent observations, specifically in the context of the extensive rollout of COVID-19 vaccines, have reignited the attention to the possibility that this uneven distribution of drugs in the SRS may lead to masking/cloaking bias, hiding disproportionality signals  [18]. The DiAna dictionary and its linkage to the ATC classification are freely available online for everyone to use (https://osf. io/zqu89/view_only=237d052047c142cabd5d8ea2e765efc6), and can be corrected and expanded by experts in the field. Changes can be proposed in the GitHub repository (https://github. com/fusarolimichele/DiAna) under the issue DiAna dictionary and will be periodically validated and integrated into the existing dictionary.
g. , "chinese food") and many choices are partly subjective. However, these choices can be defined in agreement with the entire pharmacovigilance community. The translation of ambiguous terms was also noted as a challenge, especially with overthe-counter cold, cough, and flu agents (multiple ingredients changing over the years). When we were not certain, we used the higher-level term (e. g. , "cough preparations, unspecified"). The lack of expertise in supplements and phytotherapies may have resulted in the dictionary being excessively generic (for example referring to plantago spp instead of individual species, and to covid 19 vaccines instead of specific types), and it could benefit from expert refinement for higher specificity and coverage of entries provided to other spontaneous report databases (CAERS and VAERS are more appropriate to investigate the safety profile of these medicinal products).
By incorporating a drug name-to-product translation feature, for example referring to the WHO Drug Global or to the Identification of Medicinal Products (IDMP), we could streamline the process of imputation of structured fields using free text, thereby enhancing the value of the DiAna dictionary. Linking INN names to ATC codes was a complex task due to the existence of combination products (e. g. , glecaprevir and pibrentasvir), medicinal products with ingredients that do not have an ATC code yet, and experimental substances which are missing even the INN. The linkage will be annually updated according to changes in the ATC classification to preserve its utility.
data and material: The dictionary and the linkage to the ATC classification are available at https://osf. io/zqu89/?view_only=237d052047c142cabd5d8ea2e765efc6. Code availability: All the processing, analyses, and visualization were obtained through R-software (version 4. 2. 1). The code for using the dictionary in the cleaning of the FAERS database is available at https://github. com/fusarolimichele/DiAna.
offer the DiAna dictionary as open-source tool for the pharmacovigilance community to standardize drug names in the FAERS database. Its public accessibility, transparency, and flexibility provide a foundation for ongoing improvement and refinement through input from experts in the field. With periodic updates, this open-source project can drive a common effort towards a more transparent and cleaner shared FAERS database, leading to more replicable and higher quality research in pharmacovigilance. Ultimately, by sharing and mutually enriching our knowledge, we can develop a common pharmacovigilance toolbox that advances safety and improves the accuracy, replicability and reliability of pharmacovigilance studies.
Strains C. difficile ATCC 43255 ΔPaLoc  [20]  was used for time-series NMR analyses. For studies evaluating butyrate production in stationary phase supernatants, an in-frame deletion of the hadA strain in C. difficile ATCC 43255 was generated, and uniquness of the mutant confirmed by genome sequencing of the isogenic mutant strain. The strain was shown by gas chromatography with flame ionization detection to produce no isocaproate (data not shown). Strain culture conditions C. difficile MMM was prepared as described in Pavao, et al  [20]. Preparations with labeled threonine used 15 mM L-[U- 13 C]threonine (Sigma-Aldrich) in place of natural-abundance Lthreonine. The elevated threonine concentration was selected to enable enhanced detection of minor metabolic products. Tryptone-Yeast-Glucose (TYG) media was prepared according to the formulation in Pavao, et al.
All custom code is available on the GitHub repository at https://github. com/Massachusetts-Host-Microbiome-Center/nmr-cdiff. Metabolic modeling dFBA was performed using an enhanced copy of icdf843  [20]  containing the novel threonine-tobutyrate fermentation pathway. Exchange fluxes were estimated according to the procedure in Pavao, et al. [20]. To support more accurate modeling of threonine fermentation, we made the following enhancements to the codebase: 1. Added support for non-logistic curves. In cases of intermediate accrual and consumption, as used to model 2-aminobutyrate and propionate flux, a sum of two logistic curves was used. 2. To improve modeling of metabolites with multiple metabolic origins, support was added for a sum of trajectories across separate experiments with different [U- 13 C] input substrates to constrain the flux of common metabolites generated by distinct or converging pathways.
metabolite and C. difficile metabolic gene analyses in defined-association mice. 13 C]threonine HRMAS NMR experiments performed in this study. Standard curves to estimate concentration from HRMAS NMR integrated signal used [U-13 C]butyrate, [U-13 C]propionate, and [U-13 C]1-propanol (Cambridge Isotope Laboratories, Inc. ). Ethyl [U-13 C]2hydroxybutyrate (Cambridge Isotope Laboratories, Inc. ) approximated 2-hydroxybutyrate and 2aminobutyrate signals, as neither compound was commercially available as a U-13 C preparation. dFBA was otherwise performed as described in Pavao, et al. [20]  using the COBRApy toolbox and custom Python scripts. The untargeted metabolomic datasets from Girinathan, et al. were used to evaluate levels of threonine, 2-oxobutyrate (labeled as alpha-ketobutyrate in the original dataset), 2-aminobutyrate, and propionate, in gnotobiotic mice, animals monocolonized for 7 days with P.
Nanoparticle characterization The hydrodynamic size of the resulting nanoparticles was measured using dynamic light scattering (DLS) in a Brookhaven 90Plus particle size analyzer with a detection angle of 90 •. Measurements were taken at 25 • C ± 0. 1 • C. Zeta potential measurements were performed on a Brookhaven 90Plus particle size analyzer using the ZetaPALS software in 1 mM KCl aqueous solution at a pH = 6 and 25 ± 0. 1 • C. The zeta potential was determined by studying their electrophoretic mobility and then applying the Smoluchowski equation. Transmission electron microscopy (TEM) images were recorded in a T20-FEI Tecnai thermionic microscope operated at an acceleration voltage of 200 kV. Samples were dropped on carbon coated copper grids, dried at room temperature and stained with a negative staining agent (3 % phosphotungstic acid) to improve the contrast of the polymeric nanoparticles.
Acknowledgments Financial support from the  Spanish Ministry of Science and Innovation  (grant numbers  PID2020-113987RB-I00 ,  CTQ2017-84473-R  and  PID2021-127847OB-I00 ) is gratefully acknowledged. This manuscript is the result of the projects  PDC2021-121405-I00  and  PDC2022-133866-I00 , founded by  MCIN/AEI/10. 13039/501100011033and  by the  European Union "NextGenerationEU"/PRTR. The regional Governments of Aragon and Nouvelle-Aquitaine  (by means of the Order  PRE/2106/2017  and arrêté n • 19000160  EMPIRE  project) are also gratefully acknowledged. CIBER-BBN is an initiative funded by the  VI National R&D&i Plan   2008-2011  financed by the  Instituto de Salud Carlos III  with the assistance of the  European Regional Development Fund. Authors also acknowledge the  LMA-ELECMI ICTS , for offering access to their instruments and for their expertise.