dataesr/scientific-paragraphs-categorization huggingface dataset:
24 The model allows estimation of hepatic and renal clearance in case urinary excretion data are collected. If no urinary data are collected, only one clearance term can be estimated (unless nonlinearity exist in at least one of the clearance processes). T i = V i ⋅ Kb i Q i + fu b ⋅ CL int,i (2) T i = V i ⋅ Kb i Q i (3) T i = V i Q i (4) V central dC central dt = Q p1 ⋅ C p1 Kb p1 + Q p2 ⋅ C p2 Kb p2 -Q central + CL blood ⋅ C blood For each of the lumped peripheral compartments one and two, the rate equations are defined as follow (Equation 7 and 8): Qz = ΣQi and Vz = ΣVi where z = p1 or p2 and Qi and Vi are the blood flow and volume of the ith-tissue lumped in the compartment z. The volume of distribution at steady-state based on whole blood (Vss,b) can be calculated as (Equation  9 ): (5) C blood = C central ⋅ V central V arterial + V venous + V lung ⋅ Kb lung (6) C lung = C central ⋅ V central (V arterial +V venous ) Kb lung + V lung (7) V p1 ⋅ dC p1 dt = Q p1 ⋅ C central Kb central - C p1 Kb p1 (8) V p2 ⋅ dC p2 dt = Q p2 ⋅ C central Kb central - C p2 Kb p2 (9) V ss, b = V central + V p1 ⋅ Kb p1 + V p2 ⋅ Kb p2 T A B L E 1 Tissue time constants for a reference man (70 kg) and rat (250 g)  [47] [48] [49] [50].
Many clustering algorithms have been proposed and described in the literature. 26, 27 Among them, the k-means and hierarchical clustering are the most widely used. Given the utility and limitations of each method,  27, 28  both were explored here. The tissue similarities were assessed using two different datasets (Figure  1 ): (a) tissue composition data (rat and human) or (b) using normalized in vivo Kpu values (rat data only). More details about the clustering analysis can be found in Appendix S1.
Diazepam data The models were fitted to diazepam PK data in rats and humans. Individual and average PK profiles following intravenous administration were available in humans (35 profiles)   and rats (6 profiles). Diazepam has logP and pKa values of 2. 82 and 3. 4. 29 Fup and BP were measured internally, and values were 0. 009 and 0. 559 for humans and 0. 1 and 0. 836 for rats (details in Appendix S1). Urinary excretion fraction was reported to be 0. 0005 in humans  30  and 0. 009 in rats. 31.
An exponential model was used to account for interindividual variability (IIV). 33 odels were evaluated using standard goodness-of-fit (GOF) diagnostics, numerical evaluation, and decrease in objective function value (approximated by -2*Log Likelihood). 34 Model comparison was made using the Bayesian information criterion (BIC). The biological plausibility of parameter estimates was evaluated against existing data, in particular, rat Kpu values of diazepam available from the literature. 35 An analysis of the distribution of the prediction success using RR model on a large dataset of rat Kpus showed that 95% RR Kpu predictions are found within ~21fold error (Appendix S1). Kpu values predicted beyond 21-fold were considered less likely to occur. A common study design was simulated using the R package "RxODE,"  36  allowing the comparison of the different models and study designs.
Additionally, clustering analysis was performed based on experimental rat Kpu values. Tissue grouping was based on frequencies of tissue assignments to a cluster, as some tissues have the probability of arising from different clusters (Figure  S1 ). The tissue groups obtained based on the dataset of Kpus were surprisingly different from the clustering analysis based on tissue composition data. Here, all the tissues were grouped together except the lungs, which was separate from the other tissues, and the two eliminating tissues (kidneys and liver), which could be further differentiated into two different groups on their own into a three compartmental model. This tissue grouping seemed to be similar to the time constants based on kinetic lumping.
Estimation of Kpu values for diazepam using the simplified PBPK models The proposed PBPK models were further investigated for parameter estimation using diazepam data in humans and rats. Initial exploration of structural models revealed that a linear two-compartment best described the concentration-time profiles of diazepam in humans whereas a linear three-compartment model best described the rat data. A clearance value of 3. 71 L/h and a total volume of distribution of 145 L (~2 L/kg) were estimated in humans. In rats, the clearance and total volumes were estimated to be 0. 920 L/h and 0. 914 L (~4 L/kg). These values were overall in good agreement with values reported in published studies (Tables  S4  and S5 ). These empirical models served as reference to compare the PBPK model predictions.
25 Additionally, a previous study observed correlation between muscle Kpu and other tissues Kpus and proposed to have muscle Kpu represent lean tissues. 41 To formalize this idea, a clustering analysis was performed to identify tissues with common Kpu values or scalars thus reducing the number of unknown parameters while keeping the kinetic structure of the PBPK model. Future work could aim to combine kinetic lumping and clustering analysis to cluster tissues with the same time constants. Clustering methods were applied to formally group tissues using two different types of information: human/ rat tissue composition data or rat normalized Kpu data. For the former, the assessment of tissue composition data was based on the RR model, whereas tissue partitioning equations are evolving with better mechanistic understanding (e.
g. , inclusion of lysosomal compartment  42  ). For  latter, the tissue composition are a hybrid of measured, calculated, approximated individual or mean values. 19, 20, 43, 44 Values could originate from different strains or even taken from another species especially for human values due to limited data available. Even when experimental rat Kpu data are available, various methods are used for their determination. Kpu data are ideally derived from in vivo studies following a constant rate infusion of drug until steady-state is supposedly reached or plasma and tissue areas under concentration time profiles following an i. v. bolus. Nevertheless, these in vivo Kpu methods require at least one animal per timepoint. The differences in tissue grouping between the clustering using rat experimental Kpus compared to the clustering based on tissue composition data, could be due to the dataset size, the data availability (high percentage of missing data), the quality of reported experimental Kpus, as well as the varying source of studies and methods used for measuring Kpus.
This limitation due to experimental design of studies can influence the grouping of Kpus. In this work, sources and quality of diazepam i. v. data were varied (rich data in humans, and more limited and variable data in rats). Data in humans showed better bioanalytical resolution and slower kinetics, which may reveal kinetic phases that are hidden in rats. It should be noted that the quality of the plasma PK data used for fitting was not ideal as the data were mostly arithmetic mean data, which may misrepresent the terminal phase in addition to coming from multiple studies following different protocols in different populations. Thus, the recommendation would be to perform this analysis on individual data or on geometric mean data if data are aggregated. Compared to previous works where diazepam data was fitted in man,  35, 45, 46  the models investigated here have the advantage of not requiring a highly dimensional PBPK model structure and the use of prior information or full Bayesian estimation methods for estimating the multiple parameters.
Statistical analysis Categorical variables are expressed as numbers and percentages; continuous variables are expressed as median and range. Statistical analysis was performed using SPSS Version 22. 0. The Fisher's exact test was used to compare categorical data between two groups. The Mann-Whitney U test was used to compare quantitative data between two groups. P-values below 0. 05 (two-tailed) were considered significant in all analyses. J o u r n a l P r e -p r o o f.
Acknowledgments: We thank  Stephanie Loges  and  Nicole Henjes  from the  autoantibody laboratory of Hannover Medical School  for centralized autoantibody assessment. We also thank  Cathrine Guettier  from  Hépatologie et Transplantation Hépatique, Hôpital Paul Brousse  for handling the liver biopsies. J o u r n a l P r e -p r o o f.
A pulsed laser diode LDH-P-C-375 (l = 375 nm, pulse full width at half maximum FWHM <50 ps, repetition rate 200 kHz-40 MHz) was used to excite the sample and mounted directly on the sample chamber at 90°. The photons were collected by a PMA Hybrid-07 single photon counting detector. The data were acquired by using the commercially available software EasyTau II (PicoQuant GmbH, Germany), while data analysis was performed using the built-in software FluoFit (PicoQuant GmbH, Germany). All the solvents were spectrophotometric grade.
Methods. For time resolved measurements, data fitting was performed by employing the maximum likelihood estimation (MLE) method. The quality of the fit was assessed by inspection of the reduced c 2 function and of the weighted residuals. For multi-exponential decays, the intensity, namely I(t), has been assumed to decay as the sum of individual single exponential decays (Eqn. 3): 𝐼(𝑡) = & 𝛼 ! 𝑒𝑥𝑝 " !#$ +- 𝑡 𝜏 !. 𝐸𝑞𝑛. 3 where ti are the decay times and ai are the amplitude of the component at t = 0. In the tables, the percentages to the pre-exponential factors, ai, are listed upon normalization. Luminescence quantum yields were measured in optically dilute solutions (optical density <0. 1 at the excitation wavelength) and compared to reference emitter by following the method of Demas and Crosby.
Acknowledgments M. M. gratefully acknowledges the  Université de Strasbourg , the  CNRS , the  International Centre for Frontier Research in Chemistry (icFRC) , and the  Labex CSC  ( ANR-10-LABX-0026 CSC ) within the  Investissement d'Avenir program   ANR-10-IDEX-0002-02 , the  French Agence Nationale de Recherche (ANR)  grant  ANR-18-CE06-007-01  for financial support.
Introduction Spontaneous reporting systems play a key role in monitoring the safety of drugs after they have been approved for the market, complementing drugs' safety profile obtained from clinical trials  [1]. Disproportionality analyses  [2]  are the most common statistical approach to analyze these reports and identify potential adverse drug reactions. They are used by health agencies, research centers, and pharmaceutical companies  [3] , for both signal detection (generating new hypotheses about potential adverse drug reactions) and signal refinement (characterizing unknown features of expected adverse drug reactions). Disproportionality analyses look for patterns of disproportional reporting of a specific adverse event in patients who have taken a certain drug, when compared with the overall rates in the database  [4].
These may involve clinical and pharmacological reasoning, theoretically-based design of disproportionality analyses, implementation and comparison of multiple operative choices (the so-called sensitivity analyses), case-by-case causality assessment, and integration with other data sources (e. g. , prescriptions data to approximate incidence, pharmacodynamic data to investigate the mechanism, regulatory documents to distinguish between expected and unexpected reactions)  [2, 18, 19]. However, there is no gold standard to guide researchers in the design of disproportionality analysis and a large heterogeneity exists in published studies. In this meta-research study, we investigated the strategies used to assess and enhance the validity of disproportionality signals. We did so by operationalizing a notion of signals validity, including (a) a priori plausibility of the hypothesis based on already accrued evidence, and (b) methodological robustness, verified as resistance from errors in the results when performing different operative choices and complementary analyses.
Data Extraction Process and Descriptive Analysis For each study, two authors extracted the data in parallel (MF, MI, CB, CK, ER). A pilot study was performed on five studies to train the authors in the extraction approach. Disagreements were resolved through discussion and consensus among all the authors. The same procedure-including a phase of extraction led separately by two authors, and a phase of comparison and consensus among all the authorswas repeated for the remaining studies. A descriptive analysis was performed to summarize the strategies used by the 100 articles to assess and enhance the validity of disproportionality signals.
Articles Selection and Description The articles retrieved through the systematic literature search were published between 1983 and 2019. The 100 articles randomly selected for inclusion were published between 1997 and 2019, with 70% of them published after January 2015 (see Supplementary Material 2). Among them, 26% were conceived as brief articles, 40% of the studies were published in specialized clinical journals, and 60% in pharmacological journals, with the most recurrent journals being "Drug Safety" (13%), the "European Journal of Clinical Pharmacology" (8%), and "Pharmacotherapy" (7%). Each study investigated a median of six drugs [interquartile range (IQR): 1-14] and one event (IQR: 1-5), with four articles investigating all drugs found in the database for one or more events, and three articles investigating all the events for one or more drugs.
The drugs more commonly analyzed targeted the nervous system (24%), followed by antineoplastic agents (20%) and antiinfective drugs (11%). The most frequently studied adverse events were neurological (11%), cardiac (11%), and vascular disorders (7%)  [16]. In 55 articles, a combination of MedDRA ®1 terms was used to retrieve cases. Among the analyzed articles, the most commonly used spontaneous reporting systems were the US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS, n = 40), the World Health Organization reporting system (VigiBase, n = 28), and the French national database (BNPV, n = 20). In 11 articles, more than one database was used. In 78 articles, the entire database was used for disproportionality analysis. Most common restrictions were applied, limiting the analysis to specific demographics (n = 14), therapeutic areas (n = 7), time ranges (n = 2), and serious events only (n = 2).
Complementary Analyses In 25 articles, the authors complemented the analyses on spontaneous reporting data with information from other data sources (Fig. 1 ). In 23 cases, the biological plausibility was investigated by linking spontaneous reports with pharmacometrics (pharmacodynamics in 15 articles, pharmacokinetics in 11 articles, genetic data in two articles). In two articles, the authors linked drug utilization data to overcome the lack of exposure data, in the attempt to estimate reporting incidence measures. In two studies, one or more additional case reports were published and discussed together with the disproportionality analysis. Among the 17 articles performing only a raw disproportionality analysis, 5 also performed a pharmacometric evaluation.
e. , subgroup or stratified analyses), which identified the lack or the presence of a specific confounder, is recommended  [34]. The adoption of multiple frequentist disproportionality estimates (e. g. , RRR, ROR, PRR) is also debated since their results overlap in big databases  [35]. The adoption of a frequentist and a Bayesian approach instead may help to prioritize among signals, integrating the higher sensitivity of the former and the highest specificity of the latter  [36]. Furthermore, running the analyses on multiple spontaneous reporting systems usually has consistent results  [35]  and may not be necessary apart from specific drug-event pairs, depending on the database-specific pattern of use  [37]. Finally, when agnostic signal detection is performed, strategies to control for false discovery rates and to prioritize signals can also reduce spurious signal generation  [32, 38] , but there is still no consensus on criteria and thresholds  [28, 39, 40].
Case-by-Case Assessment Case-by-case analysis and causality assessment, including the evaluation of temporal and pharmacological plausibility, as well as a differential diagnosis to exclude alternative explanations, is an important step in the evaluation of safety signals by drug agencies. Even if case narratives are not always available in international databases due to data privacy policies, other data useful for causality assessment are often reported (time to onset, drug dose, action taken, and evolution). We recommend, if appropriate and whenever possible, the implementation of a causality assessment, especially for designated medical events such as Torsade de Pointes, drug-induced liver injury, and severe skin reactions, for example using the WHO causality assessment method  [25]  or Bradford Hill criteria  [20].
Clinical trials are actually in course for hepatocellular carcinoma (NCT04066660) and rectal cancer (NCT04342949). It could be useful combined with immune checkpoint blockade therapies to treat lymphoproliferative malignancies. Our results suggest that vLMW-F (<600 Da) could be effective potential adjuvants of anti-PD-L1 or anti-PD-1 immunotherapy thanks to their favorable anti-proliferative and pro-apoptotic effects associated with their ability to decrease membrane PD-L1 via actin depolymerization. Since fucoidan possesses anti-inflammatory properties, which could interfere with antitumoral functions (reduction in recruitment of antitumoral killing cells and increase in cancer cell apoptosis and chemo-sensitivity), it will be important to further investigate this activity for vLMW-F. Otherwise, the PD-L1/PD-1 axis plays a crucial role in the tumor microenvironment and interactions between antitumor and cancerous cells.
Number-average molecular weights (Mn), degree of polymerization (DP) and polydispersity index (I) were estimated by SEC-HPLC according procedures already published  [71]  using calibrant curves made of pullulans standards (Polymer Standards Service GmbH, Mainz, Germany) for native fucoidan and heparin standards (Iduron, UK) for F1 and F2 fractions. Degree of sulfation (DS) was calculated by an Azure-A-basedcolorimetric assay (Sigma-Aldrich) according to a state-of-the-art technique. Results of these characterizations are summarized in Table  1. As shown in Table  1 , the F1 and F2 fractions contain a mixture of different fucoidanderived compounds with similar very-low-mean DPs between 3. 4 and 3. 5 but are distinct by their mean sulfation degree, 6% against 2. 1%, respectively. Further, LC-MS (data not shown) confirmed that these fractions contain predominantly di-and tri-oligofucoidan with, interestingly, several unsaturated species.
Cell Cycle Analysis LCLs and DLBCLs cells were seeded in plates (5 × 10 5 cells/well) for 24 h and then treated with 100 µg/mL of native fucoidan or F1/F2 fractions. After 48 h, cells were collected, washed twice in Dulbecco's Phosphate Buffered Saline (DPBS-Eurobio Scientific) and fixed with ice-cold 70% ethanol overnight. For Propidium Iodide (PI-Sigma Life Sciences) staining, fixed cells were washed twice with cold DPBS and incubated in 30µL of RNase working solution (10 mg/mL) and 1 mL cold DPBS for 20 min at room temperature (RT). Then, samples were stained with PI and analyzed using a BD FACSCalibur flow cytometer and Kaluza Analysis 2. 1 Software (Beckman Coulter).
Apoptosis Analysis We followed the same protocol of seeding and treatments as described above for cell cycle analysis. LCLs and DLBCLs (5 × 10 5 cells/well) cells were collected and washed with DPBS containing Ca 2+. Then, they were stained with Annexin V-FITC (Biolegend) and PI (5µg/mL) for 15 min, in the dark, at RT. Stained cells were analyzed using a BD FACSCalibur flow cytometer and Kaluza Analysis 2. 1 Software.
After washing, the protein bands were detected with a chemiluminescence detection system (ChemiDoc TM Touch Gel Imaging System-Bio-Rad Laboratories), which were quantified and numerated using Fiji software (Rasband, W. S. , ImageJ). A ratio was calculated for PD-L1 expression/αtubulin expression, and then a second ratio was calculated for test/control to compare expression of treated to untreated cells.
PD-L1 Expression Analysis: Immunofluorescent Staining and Flow Cytometry For surface labeling, the same protocol of seeding and treatment as described above for cell cycle analysis was followed. LCLs and DLBCLs cells were collected and washed with DPBS. Then, they were labeled for 15 min in the dark at RT with anti-PD-L1-PE (Biolegend) (Table  S1 ). Intracellular PD-L1 staining was performed on LCLs and DLBCLs cells treated with native fucoidan or F1/F2 fractions using the IntraPrep Permeabilization Reagent kit (Beckman Coulter) according to the protocol recommended by the supplier. Acquisitions were performed on FACSCalibur. Results were analyzed with Kaluza Analysis 2. 1 Software. Fold change was calculated based on the mean fluorescence intensity ratio of PD-L1 on its isotypic control, and then normalized to the control (untreated cells).
F-Actin Cytoskeleton Immunofluorescence Following the same experimental protocol of treatment as described above, LCLs and DLBCLs cells were collected and washed with DPBS. After washing, they were fixed with 4% paraformaldehyde for 10 min at RT and washed with DPBS. Actin fibers were revealed using 405-Phalloidin-iFluor reagent (Abcam) as per the manufacturer's instructions. Finally, after 30 min of phalloidin incubation, nuclei were stained with TOPRO-3 (1:1000-Fisher scientific) for 15 min at RT. Cells were visualized using a ZEISS LSM 900 confocal microscope (40 × oil lens). Images were constructed using the Fiji software. For actin quantification by flow cytometry, the same experimental protocol was followed. Intracellular actin fibers labeling (45 min of phalloidin staining) was performed after permeabilization (IntraPrep Permeabilization Reagent kit-Beckman Coulter) according to the protocol recommended by the supplier.
Stock solutions of cholic acid (CA) and deoxycholic acid (DCA) were prepared in methanol and ethanol, respectively, and stored at 4° C. Final solutions (10 μM each) were prepared immediately before use in oxygenated S1 solution. All physiological measurements were performed at room temperature. Data analysis was performed with ImageJ (NIH). Time series stacks were aligned using the Template Matching plugin. Ca 2+ responses of individual dendritic knobs were normalized to the knob resting fluorescence level obtained before stimulation (ratio Fx/F0, Fx = actual fluorescence; F0 = mean fluorescence of the first 50 images of the experiment). The following criteria for stimulusinduced Ca 2+ responses were applied. (i) A response was defined as a stimulus-dependent deviation of fluorescence that exceeded twice the SD of the mean of the baseline fluorescence noise.
The test mouse was placed between the two stimulus mice and recorded for 10 min with an infrared digital video camera. All mice showed a keen interest in investigating the stimulus source during which their nose was in close contact with the stimulus animals. Investigative behavior was scored manually using Behavioral Observation Research Interactive Software (BORIS)  [58]. Stimulus mice were used in several consecutive trials. We did not observe any influence of trial number on investigation times. The avoidance index was calculated as log 2 investigation time ratio = log 2 IT PBS /IT LPS , with IT PBS as the time a mouse investigated the PBS stimulus animal and IT LPS as the time a mouse investigated the LPS stimulus mouse. Negative values represent preference for the LPS-treated stimulus animal and positive values represent preference for the PBS-treated stimulus animal.