dataesr/scientific-paragraphs-categorization huggingface dataset:
02 for the mass tolerance of fragment ions used for MN. The minimum cluster size was set to 1. In addition, links between nodes were created when the cosine score was greater than 0. 70, and the minimum number of common fragment ions shared by two MS/MS spectra was 6. Links between two nodes were only kept in the network if each node was in the top 10 most similar nodes. The molecular network was visualized using Cytoscape 3. 8. 0 software  [36]. The nodes were annotated by comparison with reference standards by spectral matching with the curated GNPS, mzCloud online mass spectral libraries, and information propagation  [37]. Figure  1  shows the overall methodology of the cross-sectional metabolic study approach used in this study.
Increasing number of Janus kinase (JAK) inhibitors have been approved for chronic haematopoietic neoplasms and inflammatory/autoimmune diseases. We aimed to assess safety of the first three approved JAK inhibitors: ruxolitinib, tofacitinib and baricitinib. In this retrospective observational study, pharmacovigilance data were extracted from the World Health Organization database. Adverse events are classified according to Medical Dictionary for Regulatory Activities hierarchy. Until February 28, 2021, all Individual Case Safety Reports [ICSRs] with the suspected drug ruxolitinib, tofacitinib or baricitinib were included. Disproportionality analysis was performed and the information component (IC) was estimated. Adverse events were considered a significant signal if the lower end of the 95% credibility interval of the IC (IC025) was positive.
We identified 126,815 ICSRs involving JAK inhibitors. Ruxolitinib, tofacitinib and baricitinib were associated with infectious adverse events (IC025 1. 7, especially with viral [herpes and influenza], fungal, and mycobacterial infectious disorders); musculoskeletal and connective tissue disorders (IC025 1. 1); embolism and thrombosis (IC025 0. 4); and neoplasms (IC025 0. 8, especially malignant skin neoplasms). Tofacitinib was associated with gastrointestinal perforation events (IC025 1. 5). We did not find a significant increase in the reporting of major cardiovascular events. We identified significant association between adverse events and ruxolitinib, tofacinitib and baricitinib in international pharmacovigilance database. For a decade, growing interest in clinical immunology and rheumatology regarding targeted therapies to block cytokines and their signaling have led to the development and use of Janus kinase (JAK) inhibitors.
Post-marketing reporting constitutes an important source to identify safety signals. In this study, we assessed the safety of the first three approved JAK inhibitors-ruxolitinib, tofacitinib and baricitinib-by using the World Health Organization (WHO) international pharmacovigilance database, VigiBase, which contains more than 24 million individual case safety reports (ICSRs) and classifies adverse events according to the Medical Dictionary for Regulatory Activities (MedDRA). To identify safety concern, we used disproportionality analysis.
7%, respectively), whereas myelofibrosis and polycythemia vera were reported in ruxolitinib ICSRs (43. 5% and 19. 3%). Adverse events. A total of 376,487 adverse events were reported in the 126,815 ICSRs (including 6179 different PTs). We identified four main System Organ Classes (SOCs) for which adverse event reporting was significantly increased for JAK inhibitors compared with the full database (Fig. 1  and supplementary Table  S2 ): "infections and infestations" (IC025 1. 7, i. e. lower limit of the 95% credibility interval of the information component estimated thanks to disproportionality analysis), "musculoskeletal and connective tissue disorders" (IC025 1. 1), "investigations" (IC025 0. 9), and "neoplasms benign, malignant and unspecified" (IC025 0. 8). Six other SOCs (including blood and lymphatic system and respiratory, thoracic and mediastinal disorders) represented also significant increased reporting of adverse event associated with JAK inhibitors (Fig.
Gastrointestinal perforation (Table Major cardiovascular events (Table  3  and supplementary Table  S7 ). No major cardiovascular adverse event were associated with higher reporting for JAK inhibitors. Similarly, no cerebrovascular events were reported with JAK inhibitors. At the drug level, only ruxolitinib had a significant increase of reporting for adverse events "cardiac failure", "cardiac failure acute", "cardiac failure congestive" and "cardiac failure chronic" compared with the full database.
Discussion In this pharmacovigilance study, JAK inhibitors were most commonly associated with infectious adverse events, embolism and thrombosis, neoplasms and gastrointestinal perforation events. We also identified significant increase in adverse event reporting regarding musculoskeletal and connective tissue disorders. Finally, we found no association with major cardiovascular events. In our study, infections were frequently reported for JAK inhibitors, as was expected according to safety data from clinical trials  24, 30. We found a significant increase in reporting compared with the full database for some microorganisms (viral [herpes and influenza], fungal, and mycobacterial infectious disorders) and two main organ locations (respiratory and urinary tract infections). Herpes zoster has been identified as a complication of JAK inhibitors in clinical trials  25, 26, 31, 32  and in a pharmacovigilance study of adverse events reported from the United States  22.
Methods Study design and data sources. In this retrospective observational study, pharmacovigilance data were extracted from VigiBase, the World Health Organization (WHO) database of adverse drug reactions reporting, which is managed by the Uppsala Monitoring Centre (UMC). It contains more than 24 million individual case safety reports (ICSRs) submitted by national pharmacovigilance centers from countries around the world since 1967. Different people can report adverse drug reactions: healthcare professionals, patients, pharmaceutical companies. For each ICSR, characteristics of the patient, general administrative information, drugs and reactions are available. A completeness score is also provided, to add a measure of ICSR quality  44. The likelihood of a causal association is not the same in all reports.
A severe adverse event could be any event causing death, being life-threatening, requiring initial or prolonged hospital stay, or leading to persistent or clinically significant disability, congenital anomaly, birth defect or any other medically important condition. Statistical analysis. To identify potential safety concern, we used disproportionality analysis, which compares the proportion of each suspected drug-induced adverse event (at different MedDRA levels) reported for a drug or a group of drugs with that for the same adverse event in the full database or for other drugs. Thus, when a proportion of an adverse event is higher for JAK inhibitors than for other drugs, this adverse event could constitute a safety concern. Two main estimations of the disproportionality analysis can be used: the information component (IC) for comparing to the full database or to other drugs and reporting odds ratios (RORs) for comparing drugs belonging to the same group of drugs.
Introduction Because patients and patient organizations want to strengthen their role in the care pathway and drug evaluation and in order to improve pharmacovigilance activities, European competent authorities implemented regulations to allow direct reporting of adverse drug reactions related to medicinal products by patients in 2012. Objectives To describe evolution and analyze determinants of patient reporting activity in France in order to assess patient involvement in pharmacovigilance. Method Using the French national pharmacovigilance database, univariate and multivariate analyses were performed to compare the characteristics of adverse drug reaction (ADR) reports from patients and healthcare professionals (HCP) between 2011 and 2020. The relationship between regional patient ADR report activity and regional care provision and socio-professional characteristics was analyzed using the principal component analysis.
Introduction Pharmacovigilance aims to monitor, evaluate, prevent, and manage the risk of adverse drug reactions resulting from the use of the medicinal products  [1]. It therefore looks for detection of unexpected adverse drug reaction (ADR) to promote a better use of drugs, thus a reduction of risk for each exposed patient. To achieve this goal, surveillance activity relies on spontaneous ADR reporting, which remains the cornerstone of the early detection of safety signals  [2]. Since 1984 in France, the reporting of any adverse reaction that may be due to a drug of which they are aware is mandatory for any physician, dental surgeon, or midwife. This obligation was extended to pharmacists in 1995  [3]. Then, these ADRs are reported to the regional pharmacovigilance centers (CRPV) using the official form  [6]  and they are evaluated and recorded into the French Pharmacovigilance database (BNPV) since 1985.
Data collection The French pharmacovigilance database contains individual case safety reports (ICSR) reported to the CRPV. These centers are located in the 13 regions of France. These ICSR come from healthcare professionals or patients and patient organizations  [16]. Medicinal products are listed as specialty or active substance based on available information. ADRs are coded according to the international terminology of the Medical Dictionary for Regulatory Activities (MedDRA). Our study included spontaneous declarations notified from January 2011 to December 2020. Reports corresponding to follow-up were not considered. For this study, reports from overseas territories were not included, and Corsica was included in the Provence Côte d'Azur region. For each notification, the following variables were extracted: sex, age, region, notification date, notified type, start date, effect type (e.
Comparison of patient versus healthcare professional (HCP) reports A descriptive analysis of the population study on patient (age, sex), report (tool, delay, period, and reporter), and ADR (pregnancy related, medication error) characteristics was conducted and categorized as a healthcare professional or patients. Descriptive results are expressed either as arithmetic means with standard deviation (SD) or median with interquartile interval (IIQ) or as percentages. The chi2 test was performed to compare the observed percentages. Mean and medians were compared using the Wilcoxon test. Missing data were not considered in the analyses. Multivariate analysis to compare patients versus health professionals was conducted using logistic regression. The independent variables (sex, age classes, severity, reporting modality (web portal versus others: letter, telephone, CRPV website, and CRPV visit), and reporting time) that were significant (p value < 0.
Correlation between patient reporting activity and regional characteristics The inter-relationship between regional features and patient reporting activity was performed using the principal component analysis (PCA). The PCA  [19, 20]  is a multivariate method of analysis that explores multidimensional datasets of quantitative variables. It allows to synthesize the information into only a few new variables called components. The Kaiser criterion was chosen to determine the number of axes for the analysis  [19]. Only components with a variance or eigenvalue > 1 were retained. Each component was characterized by its loading or correlation with the original variables, which, in this case, were regional reporting activity and regional characteristics (socio-professional categories, gross domestic product, age classes, physician density, and number of inpatient beds).
Top 10 reporting A top 10 list of the most reported drugs by patients and healthcare professionals was drawn as follows: prior to 2017, from 2017 through web portal reporting and from 2017 via the standard reporting tools ("other"). According to the number of reports, the 10 first drugs were ranked for "patients" and for "healthcare professionals. " All analyses were performed using SAS ® studio software version 9. 4. Multivariate analysis of the reporting characteristics between patient and professional confirms a difference on the female sex (OR = 2. 08, CI = 2. 02-2. 14), the use of the web portal AE reporting (OR = 49. 35, CI = 47. 89-50. 86), and the higher delay for patient reporting (OR = 1. 52, CI = 1. 49-1. 57).
Two µL of sample were injected and LC separation was performed on an Acquity UPLC BEH C18 column (2. 1 x 100 mm, 1. 7 µm; Waters Inc. , MA) with a binary gradient of elution at a flow rate of 0. 5 mL/min, consisting of ammonium acetate 10 mM/0. 1% formic acid (solvent A) and 0. 1% formic acid in acetonitrile (solvent B). The gradient started with 5% solvent B for 1. 5 min and then ramped up to 30% solvent B over a period of 3. 5 min, and up to 40% solvent B over another period of 3 min and finally up to 95% solvent B within 0. 5 min. The gradient was then maintained at these conditions for 0. 5 min before recycling back to 5% solvent B in 1 min and then held at 5% solvent B for 1 min. The MS/MS data were acquired in multiple reaction ion monitoring (MRM) mode using positive electrospray ionization.
Molecular networking generation Molecular networking (MN) applied to untargeted screening is an efficient tool for visualization and organization of MS data of unknown compounds in a graphical form and is based on spectral similarity. The generation of a molecular network requires an analysis by LC-HRMS/MS, then a reprocessing of the data by various software accessible in open source. Concerning the analytical part, LC was performed on a Accucore Phenyl Hexyl column (100 x 2. 1 mm, 2. 6 μm) (Thermo Scientific, San Jose, CA), MS analyses were performed using Orbitrap Q Exactive™ mass spectrometer coupled to an UltiMate 3000 pump (Thermo Scientific, San Jose, CA) and a heated electrospray ionization source (HESI-II) was used for the positive ionization of the target compounds as already described  [13, 14, 16, 19].
Concerning the reprocessing of data by open source bioinformatics software, the complete MN approach is already described elsewhere  [20]. Briefly, raw spectral data converted in a. mzXML (ProteoWizard's MSConvert module) open mass format are preprocessed using a MZmine 2 software  [21]. These preprocessing steps are required upstream of multi-sample MN generation, and include deconvolution, de-isotoping, alignment, gap-filling and annotations. The ensuing spectra file are saved as a single. mgf output file and loaded on the Global Natural Products social Molecular Networking (GNPS) web platform creating the multimatrix MN  [22]. Lastly, the visualization of the obtained molecular network is done with the Cytoscape 3. 8. 0 software  [23] , thus simplifying interpretation of the results. Following settings were selected for this study: mass tolerance of precursor and fragments ions used for MS/MS spectral library searching set to 0.
Gastric contents also contained a significant concentration of carbofuran, 3-OH-carbofuran and 3-keto-carbofuran in comparison with cardiac blood (Table  3 ). In twin brother 2 (alive), neither alcohol nor drugs were detected in biological samples, excepted alprazolam at a low concentration and midazolam, which was given during hospital treatment. Carbofuran and its metabolites were predominantly found in urine, with 3-hydroxycarbofuran having the highest concentration. In addition, only carbofuran and 3-OH-carbofuran were found in peripheral blood (Table  3 ). Among carbofuran metabolites, only 3-OH-carbofuran and 3-keto-carbofuran were quantified. Thus, we used MN as a bioinformatics data reprocessing approach to analyze the unquantified metabolites. By displaying MS/MS data acquired during analysis, MN showed clusters containing nodes corresponding to carbofuran (m/z 222.
A MN approach enabled us to efficiently compare datasets by highlighting the distribution of carbofuran and its metabolites in the different matrices in a semi-quantitative manner. In addition to quantitative results, we found that 7-phenol-carbofuran and 3-OH-carbofuran glucuronide were predominantly excreted in urine, but also in bile. Overall, these results confirm the available pharmacokinetic data in humans highlighting high urinary elimination of carbofuran metabolites and show the particular interest of 3-OH-carbofuran, 7-phenol-carbofuran and 3-OH-carbofuran glucuronide as relevant consumption markers in urine  [10]. In addition, we show that bile is a very interesting matrix for demonstrating carbofuran consumption in postmortem settings due to the high intensity of metabolites in this matrix.
The blood and urine of the living twin were collected a few days after the carbofuran ingestion, leading to concentrations that do not reflect acute intoxication. Since we do not know whether the twins were homo or heterozygous, a difference in CYP polymorphism explaining a difference in susceptibility to carbofuran toxicity between twins cannot be excluded. A different metabolic associated fatty liver disease state between the two individuals, leading to a different metabolism of carbofuran, may have explained a difference in toxicity  [29, 30]. These data are supported by the appearance of hepatic steatosis found at autopsy in twin brother 1, although we do not have similar data in twin brother 2. The different pathologies accumulated throughout life could also have predisposed twin brother 1 to a susceptibility to carbofuran intoxications.
Conclusion We describe here an original case of intoxication of two twins by the same product Diafuran 5G®, which we quantified as containing 5% carbofuran. Concentrations of carbofuran and its main metabolites were measured with an original LC-MS/MS method in biological samples and analyzed regarding the literature. Using a MN approach, we brought new data regarding carbofuran metabolite distribution especially in postmortem settings. As a result, we confirmed in this original case the particular interest of bile samples in post-mortem analysis when urine is not available and interest of metabolites identification/quantitation in context of carbofuran intoxication. From an analytical point of view, we highlight that the detection and interpretation of ammonium adducts allows to confirm the identification annotations of the metabolites, as well as their structural data.
Particle Size Distribution, Zeta Potential and Morphology of AEM-CS Nanoparticles The size distribution and ζ-potential values of AEM-CS nanoparticles are described in Table  3. Dynamic light scattering technique is used to determine the size distribution profile of nanoparticles  [35]. Results revealed that the average hydrodynamic diameter of nanoparticles formulations was in the range of 211-269 nm with an acceptable PDI range of 0. 279-0. 485. Particle size was smallest when the AEM-CS ratio was 1:1 (Figure  3A ). The polydispersity index (PDI) is a dimensionless parameter that tells us the heterogeneity in the dispersion of detected particle size. PDI values less than 0. 1 are considered as "Monodisperse" and PDI values greater than 0. 7 are considered as "Polydisperse"  [36]. PDI results were calculated by data obtained from DLS analysis.
Fourier Transform Infrared (FTIR) Spectroscopy Spectral investigations of AEM-CS nanoparticles, native polymer (AEM and CS) and drug (MTX) were recorded in the region 4000-400 cm -1 by IR Prestige-21 spectrophotometer (Shimadzu, Kyoto, Japan). This analysis was made for the identification of any interaction between polymers and drugs  [64, 65]. The particle size, polydispersity index (PDI) and zeta potential evaluation of MTX-AEM-CS NPs, with 1 mg/mL concentration, was performed by a Zetasizer Nano ZS (Malvern Instruments, Malvern, UK) at room temperature  [66]. For this investigation, dilute solutions were employed, as concentrated ones lead to false measurements. The inaccuracy in particle size study in concentrated solutions is mainly owed to the multiple scattering  [67]. Cumulants method of analysis was used in Malvern software to analyze the data.
With appropriate refractive indices for the bulk suspension droplet, this software considered each particle as a sphere and considered that in bulk distribution. To avoid the tendency of particle aggregation, a 0. 2 µm syringe filter was used to add the samples. Each formulation was analyzed thrice and the collected results were averaged. Data obtained from the same software was also used for the analysis polydispersity index  [68]. Polydispersity index is calculated by dividing the square of standard deviation with average particle diameter by Equation  (1). PDI = (σ 2 /d) (1).
Morphological Analysis The analysis of morphology and surface of optimized MTX loaded AEM-CS NPs was made by FEG-SEM EM8100F (Field Emission Gun Scanning Electron Microscope equipped with EDX Analyzer) with an accelerating voltage of 20 kV. Image J software was used for the analysis of SEM micrographs. The samples were placed on double-sided tape secured on aluminum stubs and scanned at 15 KV after gold coating  [68, 69]. The micrographs were acquired after adding one drop of NPs suspension over copper grid accompanied with negative attaining by uranyl acetate at 60 kV accelerating voltage  [58].
S9D ). Of note, Ruthenibacterium lactatiformans would correspond to the Ruminococcaceae UBA1819 signal identified by 16S rRNA according to the SILVA rRNA database. Based on our results showing a shift in microbiome composition post-castalagin, we investigated the impact of castalagin on metabolite production. Mice were treated with either 14 days of broad-spectrum ATB conditioning or water. MCA-205 tumor cells were then inoculated, and each group was subdivided and treated with castalagin or PBS. Metabolite extraction and measurement by a combination of chromatographic and mass spectrometric methods was performed on the serum, feces, tumor, liver, ileum, and colonic contents. Metabolite production post-CC supplementation was divergent in water compared with ATB groups in the feces and serum, supporting the microbiome-dependent mechanism of castalagin (Fig.
bromii relative abundance in both GF and SPF mice feces (Fig. 7G ; Supplementary Fig. S18B-S18D ). Finally, in a proof-of-principle experiment, we analyzed the feces of two non-cancer patients enrolled in a clinical trial (NCT04058392) addressing the safety of oral administration of CC  (26). Preliminary fecal metagenomics revealed a positive trend in diversity and toward enrichment of R. bromii, consistent with the results obtained in CC-treated mice (Supplementary Fig. S18E ).
bromii were associated with an increased proportion of CD8 + T cells in the tumor and peripheral blood and correlated with a beneficial response  (12). In addition, Ruminococcus bacterium cv2 is one of 11 beneficial bacteria present in the VE800 probiotic capsule that was linked to increased IFNγ + CD8 + T cells in the colon and tumors and is currently under investigation in a clinical trial (NCT04208958; ref. 14). Specific bacteria modulating ICI outcomes are further demonstrated with A. muciniphila, which predicted the response in 153 patients with advanced NSCLC and renal cell cancer amenable to ICI  (10). This observation was recently validated in a prospective trial of 338 patients with NSCLC  (25). In this study, R. bromii was the most abundant bacteria in patients with detectable A.