dataesr/scientific-paragraphs-categorization huggingface dataset:
Pharmacokinetic analysis Pharmacokinetic analysis was performed using a non-parametric adaptive grid approach (Pmetrics* software for R, Version 3. 3. 1) (Neely et al. , 2012). In the building model, data were fitted using one-and twocompartment models. The choice of the best pharmacokinetic model was based on the Akaike criterion, mean bias between individual predicted C3 and observed C3, weighted residual plot (observed C3 -individual predicted C3), and comparison of individual predicted pharmacokinetic profiles for each patient generated by the one-and two-compartment models. The influence of NAT2 status, age, weight and gender on each pharmacokinetic parameter in the retained model was tested using linear multiple regression. NAT2 status was tested as a dichotomous variable [individuals with two slow alleles (SA) and individuals with at least one rapid allele (RA/IA)].
Statistical analysis Statistical analysis of the data was performed using SPSS 18. 0 (IBM Corp. , Armonk, NY, USA). Continuous variables were expressed as mean AE standard deviation if data were normally distributed, and expressed as median if data were non-normally distributed. Distributions of continuous data were assessed using the Kolmogorov-Smirnov normality test, and parametric or nonparametric tests were applied as appropriate. P < 0. 05 was considered to indicate significance.
External validation of the final model The precision of the final model was evaluated using an external dataset (28 patients). In this population, %MPE and %MAPE were -5. 6% (range -36 to 9. 7%) and 10. 46% (range 0. 51-36. 4%), respectively, between observed and individual predicted C3. % MAPE was <20% in 24/28 (85. 7%) patients. %RMSE was 13. 7% between individual predicted C3 and observed C3.
The second limitation was the use of the spectrocolorimetric method, which is considered an outdated analytical approach. The use of a chromatographic method in a future application of this model in an external dataset could be helpful to assess whether this model could be applicable for chromatographic determination of INH concentration. In conclusion, this was the first study to establish a pharmacokinetic population model to enable optimization of individual dosing regimens of INH in patients with tuberculosis in Tunisia. NAT2 acetylator status was the only covariate found to affect INH pharmacokinetic parameters significantly. The present dose proposal based on PTA may be helpful to achieve a therapeutic concentration of INH in a shorter time, and could be used as guidelines in INH TDM in order to reduce the risk of toxic side-effects in SA patients and early treatment failure in RA patients.
Reactive Oxygen Species (ROS) Production To better understand how the brown algae extract acts on A549 cells, subsequently we assessed ROS production after incubation with B[a]P. B[a]P 1 µM significantly elevated ROS production compared to control after 48 h (×1. 19, Figure  5 ); that overproduction was totally inhibited by the brown algae extract. Whatever the B[a]P concentration used, the basal level of ROS production was significantly lower when the cells were incubated with the algae extract. B[a]P did not induce ROS overproduction after 72 h (data not shown). CYP1A1 and 1B1, CYP2C isoforms of ROS make them very challenging to detect. For example, the lifetime of singlet oxygen, superoxide, and hydroxyl radical in aqueous solutions is within the µs regime  [26]. Libalova at al. has previously shown that BaP exposure has a very limited effect on ROS generation in A549 cells  [27].
Disclosure may be incomplete or false, by deliberate or unintended omission. Omission may impact the analysis of results or at least the reader's perception of it. The rate of validity of disclosure in OTSR is not known, and we therefore conducted a bibliometric study on articles published in 2017, in order to: 1) determine the proportion of articles in which authors disclose interests; and 2), when interests are disclosed, to assess their validity by comparison against the Transparence. gov. fr database. Hypothesis: Disclosure rates exceed 75%. Material and method: We analyzed all articles published in OTSR in 2017, and extracted those with at least one French author. We also analyzed data from the Transparence. gouv. fr corporate declaration database, for comparison with the authors' own disclosures in each article.
This disclosure of interest might be incomplete or false, by deliberate or unintended omission. Such omission could bias the interpretation of the results, or at least the reader's perception  [14]. As the rate of validity of the disclosures of interest in OTSR is not known, we conducted a bibliometric study on articles published in 2017, in order to: 1) determine the proportion of articles in which authors disclose interests; and 2), when interests are disclosed, to assess their validity by comparison against the Transparence. gov. fr database. The study hypothesis was that declaration rates exceed 75%.
Material We analyzed all articles published in OTSR in 2017 and extracted those with at least 1 French author. We also analyzed data on the www. transparence. sante. gouv. fr health industry transparency website of the French government for comparison with the disclosures of interest made by the authors of each article. Homonyms were searched for and grouped together manually. All analyses were made by 2 investigators (RE and TH), so as to double-check data, with discussion in case of disagreement (in 3 cases).
We defined interests according to increasing payment thresholds: ≥ D 0, > D 1000, > D 5000, > D 10,000 and > D 100,000. These thresholds, going from very small to very considerable amounts, enabled analysis of whether disclosure validity was associated with the amount of payment. They corresponded to the thresholds reported in the Transparence database for each activity declared by a company; thus a given author could have different interests at different thresholds for a given company.
0%) included ≥ 1 author with > D 10,000 interests, but in 109 of these 145 articles (75. 1%) disclosure was incomplete and authors had failed to disclose at least 1 relation of direct or indirect interest (Table  1 ). At the D 10,000 threshold, 60 of the 145 articles included a trade-name, with correct disclosure of a direct relationship in 54 cases (90%). Disclosure was valid with respect to the  transparence data for both direct and indirect interests in only 20 articles (13. 7%). Forty five of the 210 articles with ≥ 1 French author (21. 4%) articles included ≥ 1 author with > D 100,000 interests, but in 22 of these (48. 9%) disclosure was incomplete and authors had failed to disclose at least 1 relation of direct or indirect interest (Table  1 ). At the D 100,000 threshold, 26 of the 45 articles included a tradename, with correct disclosure of a direct relationship in 25 cases (96%).
Publicizing data for conflicts of interest may seem like hanging out one's dirty washing and be badly received by both practitioners and the public, but it has the advantage of enabling comparison between journals, although it also facilitates media exposure. Comparison with the literature reveals that disclosure rates in OTSR were relatively low. In 2020, Somersom et al. [16]  reported > 90% disclosure in the Journal of Shoulder and Elbow Surgery. Other journals find poorer rates, as in the present study. In 2021, Tisherman et al. [17]  reported a disclosure rate of just 16. 6% In Injury. In hand surgery, in 2020, Ross et al. [18]  reported 58% non-disclosure in the American Journal of Hand Surgery. In spine surgery, in 2020, Tisherman et al. [19]  reported 41% correct disclosure in Spine.
In the present study, all sums disclosed in the articles could be traced on the Transparence website, suggesting a certain exhaustiveness. On the other hand, there were many homonyms requiring considerable analysis ahead of data interpretation. Another website, Euros For Docs (https://www. eurosfordocs. fr/explore), also details conflicts of interest, but we did not make use of this as it is not an official site. Despite these limitations, the present study highlighted the importance of vigilance with respect to articles published in OTSR. This relies on the authors' honesty and care taken by the editorial board to check exhaustive disclosure. The present study did not assess the validity of the actual articles, and casts no aspersions on the quality of the studies published  [23] [24] [25] [26] : the spotlight was solely on disclosure of interest.
Asymptomatic carriers were defined as individuals with malarial parasitemia of any density, absence of fever or other acute malaria symptoms, no history of recent antimalarial treatment  [55]. A semi-structured questionnaire was used to collect demographic data and information on the use of antimalarial drugs. Finger-prick blood samples were collected from each participant for rapid diagnostic test (RDT, SD Bioline) and thick blood smear microscopy diagnosis. Additionally, blood samples were spotted on a Whatman 1 grade17 filter paper (Whatman 1 Grade 17 Chr Cellulose Chromatography Paper, GE Healthcare, Chicago, USA), air-dried and stored in silica gel for further molecular analyses. The study participants who were found parasite-positive received free treatment with Artesunate Amodiaquine (ASAQ) according to the national guidelines by the National Malaria Control Program (NMCP).
Data management and statistical analysis Data were recorded in Microsoft Excel (Office 2010) and analyzed using the R Core Team (2020) and GraphPad Prism5 (San Diego, CA, USA) softwares. The site maps were drawn using R, maptools and maps packages with data source from OpenStreetMap. Information from OpenStreetMap is made available under the Open Database License. Graphics were built using ggplot2 package. Alleles corresponding to the Pfdhfr/Pfdhps SNPs were combined for each individual and genotypes recorded. Comparison of prevalence of infection and frequency of antimalarial resistance mutations between the two study areas was computed using the Chisquare and Fisher's exact tests. The level of statistical significance was set at a value of P � 0. 05. The sample size was calculated by the Lorentz formula: n = T2 x P (1-P) / M2  [58]  where n = required sample size, T = 95% confidence level (standard value is 1.
Acknowledgments We would like to thank the people who agreed to participate in this survey as well as the health staff of the districts of Mfou and Tibati, the  Malaria Research Unit of Centre Pasteur of Cameroun  and the  UMR MIVEGEC in Montpellier, France , for their support and cooperation during the study. We also express our gratitude to the  French Embassy in Cameroon  who granted a 6-month fellowship to AGBT for a scientific research exchange at the UMR MIVEGEC in Montpellier.
Absorption, Distribution, Metabolism, and Excretion Study A previously published clinical trial (NCT03315559) was designed to characterise the human absorption, metabolism, excretion and pharmacokinetics of the inhaled drug nemiralisib using an intravenous microtracer, when combined with an inhalation dose and an oral radiolabel dose  [19]. Briefly, healthy male subjects underwent two treatment periods with a 14-day washout between treatments. In treatment period 1, subjects inhaled 1000 µg of non-radiolabelled nemiralisib as a dry powder from the ELLIPTA ® inhaler device, and within 5 min received 10 µg [.
Inhalation Study A previously published phase I clinical trial PII116617 (NCT01762878) explored the PK/PD relationship of nemiralisib in a single-dose escalation design  [3]. Briefly, regular cigarette smokers underwent a four-way crossover, with each subject receiving placebo, 0. 1, 0. 5, and 2 mg of a dry powder formulation of nemiralisib administered using the Diskus ® device, with a 14-day washout between treatments. A 14-day repeat-dose arm was conducted in a further cohort where the subjects received 2 mg of nemiralisib or placebo in a parallel group design, also delivered as a dry powder formulation using the Diskus device. In addition to blood samples, bronchoscopy for the collection of bronchoalveolar lavage (BAL) samples was performed at 24 h (trough) after the final (day 14) dose  [3].
Physiologically Based Pharmacokinetic Modelling GastroPlus version 9. 7 (Simulations Plus Inc. , Lancaster, CA, USA) with the nasal-pulmonary additional dosage route module was used for the PBPK modelling and simulation. In addition, MembranePlus ® version 2. 0 (Simulations Plus Inc. ) was used to predict enterocyte drug binding in the gastrointestinal tract and ADMET Predictor ® v9. 0 (Simulations Plus Inc. ) was used to predict other input parameters as detailed in Table  1. Diagrams of the individual components comprising the PBPK model can be seen in Fig. 1. The simulation of inhalation administration requires an accurate description of drug deposition, absorption from different lung regions, absorption of the swallowed portion from the intestine, and systemic distribution and clearance, each of which are described below.
Systemic clearance for nemiralisib in human from the intravenous microtracer data observed in the ADME trial (NCT03315559)  [19]  was used directly in the simulations. As nemiralisib is a basic compound, it is expected to interact with tissue acidic phospholipids  [22]. In Gastro-Plus, the human tissue acidic phospholipid concentrations are generally assumed to be the same as the rat, therefore the values were updated with those for human specified in the Simcyp TM Simulator version 18 (Certara, Sheffield, UK).
In the nemiralisib clinical study 206764 (NCT03315559), Fg (fraction escaping gut wall metabolism) was reported as 0. 83  [19] , and intestinal FPE for the model was accordingly set at 17%. The P450 enzyme predominantly responsible for the metabolism of nemiralisib is P4503A4  [21] , but it was not specified in the model. To achieve the best prediction of the oral plasma concentration profile, the effective permeability was manually fitted for each individual.
Initial predictions for the deposition of nemiralisib following inhalation administration were obtained using the Multiple Path Particle Dosimetry (MPPD) model version 2. 92. 5 (Applied Research Associates, Raleigh, NC, USA)  [24]. The model combines cascade impaction data, lung geometry and inhalation profiles as input parameters to predict the regional lung deposition. The ratio of predicted regional lung deposition was maintained, but the absolute values were subsequently adjusted using the fraction of dose absorbed from the lung (29%) following an inhalation dose as determined from GSK study number 206764 (NCT03315559)  [19]. Deposition is a patient-specific factor  [17]  but has been assumed to be the same for all individuals in the modelling. Pulmonary solubility was measured in simulated lung fluid and used directly.
Oral Absorption A reference solubility of 1 μg/mL at pH 10 was used, and, in combination with the pKa values and solubility factor, gave an adequate description of the change in buffer solubility with respect to pH (data not shown). The bile salt effect salt solubilisation ratio was fitted at 2. 82E+4 using measured fasted state simulated intestinal fluid (FaSSIF) and fed state simulated intestinal fluid (FeSSIF) solubility data. Effective permeability was predicted to be moderate from structure (1. 04 × 10 -4 cm/s) by ADMET Predictor, but had to be lowered to 0. 26-0. 46 × 10 -4 cm/s to fit the individual oral plasma concentration versus time profiles of nemiralisib in human. Initial predictions overpredicted the absorption post the time of maximum plasma concentration (t max ) of 6 h, with the total absorption from the caecum and ascending colon predicted to be substantial at approximately 40%.
Tissue Distribution in Rat Generally, predicted Kp values can be deemed accurate if they agree with experimental values within a factor of three  [22]. As can been seen in Table  3 , using a permeability-limited tissue model with the specific PStc (0. 8 mL/s/ mL) fitted against plasma data from an earlier intravenous bolus administration PK study, adequately predicted the Rtp values for liver, kidney, spleen, muscle, lung and skin (calculated by dividing the predicted tissue concentration by the plasma concentration). For model accuracy, the observed clearance of 0. 71 L/h from the tissue distribution study was used instead of the value from the earlier intravenous bolus study of 0. 568 L/h. The impact of the specific PStc was assessed by manually adjusting the value to get the best 'fit' of Rtp values.
Discussion With PBPK modelling constantly evolving, it is now possible to include the lung as a route of drug administration and to predict plasma drug concentrations as well as concentrations in tissues. Nemiralisib is a potent inhaled PI3Kδ inhibitor that has been investigated for the treatment of COPD  [3]. Based on its mechanism of action, in particular its welldescribed inhibition of regulatory T cell function, nemiralisib could potentially play a role in the treatment of cancer; however, to achieve therapeutic impact, distribution to tissues distant from the lung following inhalation administration would be required. This manuscript describes a PBPK model in GastroPlus that includes a complete mechanistic concentrations were verified against a rat tissue distribution study. The inhaled modelling strategy employed here could be a useful tool for assessing lung retention and systemic exposure of novel compounds, both in terms of pharmacology and toxicology.
In addition, a compartmental PK model for systemic distribution was applied when modelling inhaled budesonide. Data after intravenous, oral and inhalation administration are highly desirable to allow correct characterisation of pulmonary drug absorption  [17]. Data from an ADME study (GSK study number 206764; ClinicalTrials. gov NCT03315559)  [19]  allowed such a characterisation for inhaled nemiralisib. Initial models to describe the systemic distribution following intravenous administration were built using a perfusion-limited tissue model for nemiralisib, but the plasma C max was underpredicted for most subjects. A permeabilitylimited tissue model introduced a slight delay in the tissue distribution, while maintaining significant tissue distribution but enabling a more accurate prediction of the plasma C max.
Wound Healing Assay For the wound healing assay, cells were seeded in 6-well plates. When cells were confluent, a scratch was made with a 200 µL sterile pipette tip, generating a cell-free area of approximately 1 mm in width. Cellular debris was removed by gentle washing with the culture medium and the photos of the wounds (0 h) were taken. Afterwards, the medium was replaced by the culture medium with different concentrations of the drugs and the cells were allowed to migrate for 24 h. At the end of migration experiment, another set of photos was taken, from the same regions using Phase Contrast Fluorescence microscopy (Olympus, Tokyo, Japan). The gap size was analyzed using the Image-J software (LOCI, Madison, WI, USA). To assess the migration ability of cultured cells, cell-free areas of the scratches at 24 h after scratching were subtracted from the area of the scratches at 0 h and calculated as a percentage of untreated (0 h) cultures.
Introduction Disproportionality analyses are hypothesis-generating methods that aim to detect safety signals for medicinal products (ie, putative links between drugs and adverse drug reactions) from databases containing adverse events that have been spontaneously reported mainly by healthcare professionals or patients  [1]. Such methods quantify the degree to which a drug-event pair occurs "disproportionally" compared to what would be expected if there were no association between the drug and the event  [2]. That is, they answer the question: "does the number of observed cases exceed the number of expected cases?" [3]. Yet, the actual number of patients exposed to a given drug is unknown and therefore, disproportionality analyses do not provide risk quantification and, taken alone, do not allow any causal inference to be established  [4].
Study selection and data extraction We performed a systematic search of Medline using PubMed to identify all pharmacovigilance disproportionality analyses using databases of spontaneous reports by healthcare professionals or patients. The search terms were "case-non case," "case-no case" "disproportionality analysis," "pharmacovigilance analysis" or "pharmacovigilance study. " Our search strategy enabled us to identify 435 articles, indexed in Medline between its inception and January 1, 2020. After screening titles and abstracts,230 citations were considered eligible for inclusion. We mainly excluded articles because they were methodological studies (n = 77), were not based on a pharmacovigilance database (n = 50) or did not performed a disproportionality analysis (n = 43). Then we randomly selected 100 studies (listed in the supplementary material; Figure  S1 ).
For each selected article, the presence of spin was independently assessed by two reviewers (A. M. , M. L. , B. R. , C. K. ) and any disagreements were resolved by consensus. Suspected spin in each section of the article was classified according to the categories of spin described above. We also estimated the overall level of spin in each article: a high level of spin was defined as an abstract or conclusion containing an inappropriate interpretation of the results (ie, using causal language) and otherwise the articles was considered to have a low level of spin. We also collected data on various covariables such as declared financial conflict of interest, year of publication, journal category, author characteristics and citation count.
Statistical analysis Categorical data are expressed as frequencies and continuous data are presented using medians with interquartile ranges. Agreement between reviewers were assessed by Cohen's Kappa. Association between spin and each abovementioned covariate were explored through logistic regression models, odds ratio (OR) with their 95% confidence intervals (95% CI) were estimated. A sensitivity analysis considering only articles with high levels of spin was conducted. Given the exploratory nature of these analyses a P -value < 0. 05 was considered significant, without adjusting for the multiplicity of comparisons. All analyses were performed using SAS Studio, version 3. 8 (SAS Institute Inc. , Cary, NC, USA) and R (version 3. 6. 1).
In assessing causal inference in randomized and non-randomized clinical trials, Li and colleagues found an overestimation of the strength of causal inference in the abstracts of non-randomized clinical trials  [16]. Lazarus and colleagues, quantified spin in abstract of non-randomized studies and found that 53% of abstracts used causal language to describe their results  [12]. Given the limitations inherent to spontaneous reporting databases (lack of exposure data, variable reporting of ADRs) and inherent to disproportionality analyses (inability to control for confounding, competition between drugs and events) drug-related risks cannot be quantified. Nevertheless, the concept of risk or suggesting a causal relationship, was present in the abstract or in the conclusion section of almost half of the 100 articles analyzed.