dataesr/scientific-paragraphs-categorization huggingface dataset:
Results Lumefantrine concentration-time data from 4,122 patients from 26 studies were uploaded to the WWARN pharmacology database (Fig 1 ; S1 Table ). These data were categorised into 3 different geographic areas (Africa, Oceania, and Southeast Asia) comprising 12 countries (Benin, Guinea-Bissau, Tanzania, Uganda, Kenya, Mali, Mozambique, Liberia, Papua New Guinea, Lao People's Democratic Republic [Laos], Thailand, and Cambodia). Lumefantrine concentrations were available for analysis in 4 different matrices: 2,312 patients contributed venous plasma samples, 595 patients contributed venous whole blood samples, 191 patients contributed capillary plasma samples, and 840 patients contributed capillary whole blood samples. In total, 154 patients were excluded from the analysis, 71 because of missing dosing information and 83 who took repeated dosing (i.
e. , retreatment). A further 30 patients were excluded from the evaluation of pre-treatment parasitaemia as a covariate because relevant parasitological data were missing. Venous plasma data from 1,347 out of 2,312 patients who contributed at least 2 samples was used for the development of the lumefantrine population pharmacokinetic model. The remaining 400, 278, and 287 patients contributed only 1 venous plasma sample per patient after treatment with intact, crushed, and dispersible tables, respectively, and were therefore only used for external validation and evaluation of formulation effects in a separate analysis (Table  1 ). Approximately 37% of the patients were children below 10 years of age and 3. 12% were pregnant women (median [range: interquartile range] 23. 0  [13. 1-38. The following covariates were selected prospectively and their influence evaluated using a population modelling approach: body weight, pregnancy, estimated gestational age, baseline parasitaemia, dosage (mg/kg), dose per occasion (mg), total daily dose (mg), total dose (mg), and age-for-weight z-score.
Some clinically relevant covariates were not available for all patients; these included haemoglobin (available for 2,901 patients; either directly measured or calculated from measured haematocrit  [44] ) and baseline body temperature (axillary available for 3,029 patients, tympanic available for 427 patients, and oral available for 100 patients). Pooling of temperature data (i. e. , axillary, oral, and tympanic) was considered unreliable due to potential discrepancies between study-site procedures and was therefore not performed. A  DLF, desbutyl-lumefantrine; LF, lumefantrine; LLOQ, lower limit of quantification; PD, pharmacodynamic; PK, pharmacokinetic. https://doi. org/10. 1371/journal. pmed. 1002579. t001 separated covariate analysis was performed on a subset of data in order to evaluate baseline body temperature and haemoglobin as covariates.
Pharmacokinetic-pharmacodynamic time-to-event model For the pharmacokinetic-pharmacodynamic time-to-event model, individual pharmacokinetic parameter estimates from 3,486 patients were fixed and evaluated with the treatment outcome at day 42 (Table  3 ). Overall, 93 out of 3,486 patients (2. 67%) had recrudescent infections. A Gompertz hazard model with a sigmoidal E MAX lumefantrine drug effect model provided accurate predictive power in the visual predictive check (Fig  4 ), but parameter estimates lacked precision and accuracy (Table  3 ). No statistically significant covariates were found for the pharmacodynamic parameters in the full dataset, which could be a consequence of biased distribution of covariates over the geographical regions and the small number of recrudescent infections.
Discussion The population pharmacokinetic model based on this covariate-rich dataset from nearly 4,000 patients provides an improved understanding of how body weight, pregnancy, dosage, and admission parasitaemia affect the absorption, distribution, and elimination of the most important and widely used anti-malarial therapy in current use. By applying a pharmacokineticpharmacodynamic time-to-event model to this dataset, we have made, to the best of our knowledge, the first comprehensive attempt to evaluate the relationship between pharmacokinetic factors and AL treatment outcome (PCR-corrected recrudescent malaria infections) across different geographical areas and populations. The lumefantrine/desbutyl-lumefantrine population pharmacokinetic model provides an improved understanding of the disposition effect of lumefantrine's main active metabolite, which had hitherto remained poorly characterised.
Pregnancy was a categorical/proportional covariate on F [1 + θ], dose 50 was implemented using a saturation model on F [1 -(Dose/(θ + Dose))], and admission parasitaemia was implemented using a power relationship on F [(parasitaemia/median value) θ ]. https://doi. org/10. 1371/journal. pmed. 1002579. t004 contrast to the larger sample size studied previously. Weight-for-height z-scores may be a superior proxy for acute global malnutrition, especially in relation to drug pharmacology, but height was not available for all patients in the database. Including age as a maturation factor for lumefantrine elimination clearance did not significantly improve the model fit. Maturation of enzymatic activity occurs during the first 2 years  [12] , and the lack of data in the youngest children (in the model building dataset the youngest child was 6 months old and only 4 patients were below 1 year) might explain the absence of this covariate relationship.
Study limitations The developed lumefantrine population pharmacokinetic model and subsequent evaluation of alternative dosing regimens have a number of limitations. Model development was conducted using venous plasma data from patients receiving intact tablets administered with fat. Simulations, including evaluations and comparisons of alternative dosing regimens, are consequently representative only for venous plasma after administration of intact tablets with fat. Adjustments for sampling matrix (i. e. , venous blood, capillary blood, or capillary plasma) or formulation (i. e. , crushed or dispersible tablets) were not performed since correction factors were not considered reliable due to the sparseness of data. Furthermore, all patients included in this analysis received AL with fat, eliminating the possibility to characterise and quantify the impact of concomitant fat intake on the bioavailability of lumefantrine.
The absence of artemether-dihydroartemisinin data prevented a full evaluation of AL therapy. Consequently, lumefantrine concentrations at day 7 were used as a pharmacokinetic endpoint. Nevertheless, the model remains clinically relevant considering that the vast majority of patients are treated with intact tablets co-administered with fat. After the database was closed, 7 studies meeting the inclusion criteria were published (S2 Table)  [10, 12, 13, [51] [52] [53] [54]. Two studies in Malawi and Uganda evaluated lumefantrine pharmacokinetic drug-drug interactions with anti-retroviral therapies for HIV-co-infected paediatric patients  [52, 54]. Drug-drug interaction data were not available in the pooled lumefantrine pharmacokinetic database, and therefore, these data would not have made a substantial impact on the analysis presented here.
DNA sequencing of the PCR products was performed by Eurofins Genomics (Les Ulis, France) using BigDye Terminator v3. 1 cycle sequencing kit (Applied Biosystems Courtaboeuf, France) and ABI 3730XL sequencer. Sequences were read using Chromas DNA sequencing software (Technelysium Pty Ltd. , South Brisbane, Australia) and aligned using Clustal X2 software (Conway Institute, University College Dublin, Dublin, Ireland) to search for SNPs. Polymorphisms were identified in both forward and reverse strands by comparing the sequences to the reference sequences of Pvdhfr (GenBank accession no X98123), Pvdhps (GenBank accession no AY186730), Pvmdr1 (Genbank accession no AY618622), and Pvk12 (PVX_083080).
vivax occurs  [54] [55] [56] [57]. Artemisinin-resistant P. falciparum occurs in Southeast Asia, and resistance seems to be conferred by specific mutations in the propeller domain of Kelch 13 (K13; PF3D7_1343700)  [27]. In P. vivax, Pvk12 was identified as the homologous gene of Pfk13  [39]. At present, there is no molecular indication that Pvk12 undergoes a high degree of mutations as its P. falciparum homolog, even in Southeast Asia  [39, [58] [59] [60]. The sequence data of Mauritanian P. vivax isolates also indicate the absence of polymorphism in the Pvk12 gene fragment that was amplified and analysed. Moreover, clinical and in vitro studies carried out so far do not suggest artemisinin resistance in P. vivax. Further molecular surveillance is warranted to anticipate the possible emergence of artemisinin-resistant P.
Drugs Names The set of drug names were provided by several sources: commercial drug names and international non-proprietary names associated with their ATC codes  (Skrbo et al. , 2004) , the CNHIM database Thériaque 3  , the base publique du médicament 4  , and the database Medic'AM from the French healthcare insurance 5. Thériaque was especially useful because it included short names of drugs typically used by people, such as Doliprane / Tylenol.
This observation clearly indicates that it is necessary to adapt the used medical terminology and to enrich it with equivalent expressions used by patients in messages posted in social media. Hence, our task consists of building specific lexica related to the disorders we study. We exploited two approaches: (1) the use of existing resources (Lexique. org, Wikidata and JeuxdeMots), and (2) the use of corpus-driven methods, and more precisely of distributional algorithms  (Brown clustering and Word2Vec). When we used the existing resources, we expected that the needed terms and their synonyms may have been provided by these, although they were created and maintained independently of our work and needs; while the distributional algorithms relied on the content of our corpora and on co-occurrences of words for the generation of clusters with semantically close words, because such words may occur in similar contexts.
The set of the 29 source disorders (described in section 3. 3) is referred to as seeds in this step of the methods. The existing resources and distributional algorithms were exploited individually or in combination, as presented in sections 4. 2. 1-4. 2. 6. The process of automatic indexing is described in section 4. 2. 7. We also created the reference data (section 4. 2. 8) for the evaluation of the automatic indexing obtained with these specific resources and algorithms. 4. 2. 1. Lexique. org lexique. org 6 is a lexicon built by psycholinguists. It provides links between morphologically related words, such as {nervous, nervously}. We used this lexicon to expand our set of seeds through their morphological family: words sharing the same lemma like {attack, attacks}, and words sharing their longest 6 http://lexique.
Wikidata Wikidata 7  is a semantic knowledge base of general language. It is used to structure the semantic content of Wikipedia and other Wikimedia projects. Several steps were needed before this resource could be exploited for our purpose: • Extraction of items that represent disorders. Wikidata contains properties such as subclass of disease, but they are not systematically used. Hence, we extracted items associated with the ICD-10 codes corresponding to our seeds; • Exploitation of alternative labels. We also used the property alternative label to get various designations for a given item. For instance, the item agoraphobia (Q174589) is associated with the French labels agoraphobe (agoraphobic) and peur sociale (social fear). We excluded labels that vary only by the use of diacritics, like {schizophrénie, schizophrènie, schizophrenie}, because they were normalized to the same string by our methods in the next steps, but we kept inflectional variants like {phobie, phobies} in case of mistakes by the Treetagger POS-tagger and lemmatizer.
All messages annotated as unable to decide or as misuse were afterwards verified by one of the annotators, and the annotations were modified if necessary. These three corpora, containing 1,850 manually annotated messages, provided the reference data for our study: on the basis on these annotations we created our typology of misuses, and we also exploited it for the generation of language model for their automatic detection and for the evaluation of the automatic system (section 4. 4). This reference dataset provided: 600 messages with no use of drugs, 1,117 messages with normal use, and 133 messages with a drug misuse.
Wikidata also provides semantically close items, such as attaque de panique (panic attack). Other resources can provide words that are more distant semantically, such as [convulsion (convulsion), crampe (cramp), médicament (drug), alzheimer (alzheimer), bailler Table  2  presents the results obtained when the lexica we build are used for the automatic indexing of messages and sentences. For each lexicon and each granularity (sentence or message) we give the following metrics : number of True Positives TP, Precision P, Recall R, and F-measure F. This evaluation is done on 400 manually indexed messages. This is our test corpus, since this step of the methods (enriching the lexicon and automatic indexing) is unsupervised. As expected, it is easier to index messages than sentences, because the granularity of information is finer in the second case.
ACKNOWLEDGMENTS We would like to thank the annotators who helped us with the manual annotation of misuses and indexing of messages with the ICD-10 codes,  Bruno Thiao Layel  for extracting the corpus,  Vianney Jouhet  and  Bruno Thiao Layel  for building the list with drugs names (section 3. 2), and The- Hien Dao  for the set of disorders exploited (section 3. 3). Finally, we thank the whole ERIAS team for the discussions.
Proteomics The phenotype of cryopreserved PHH cultured in the 2 cell models was determined by semi-quantitative proteomics. Samples from 3 donors were prepared at day 0 and day 14 for each of the respective in vitro culture formats to have comparable data relating to the cytotoxicity experiments. Furthermore, freshly thawed samples were snap-frozen for comparative proteomic assessment. For 2Dsw samples, cells from 3 wells of a 24-well plate were washed twice in PBS and harvested by gentle scraping. Cells were then centrifuged at 900 Â g for 7 min (4 C) and snap-frozen. All 2Dsw samples were generated at KaLy Cell. For 3D samples, spheroids from 7 plates were pooled, washed in PBS, pelleted and snap-frozen. All samples were stored at -70 C prior to analysis. All 3D samples were generated at KI.
A gradient of 2-50% ACN/0. 1% formic acid (v/v) over 90 min was applied to the columns at a flow rate of 300 nl/min. The mass spectrometer was operated in positive ion mode (Analyst TF1. 7) with survey scans of 250 ms, MS/MS accumulation time of 100 ms and with monitoring of the 25 most intense ions (total cycle time 2. 5 s). Data were searched using ProteinPilot 5 software (Sciex) against the latest UniProt database with iTRAQ as a variable modification and MMTS as the cysteine alkylating reagent. The reversed database was used as a decoy to determine the false discovery rate (FDR) for protein identification, and only those proteins identified at >1% FDR were evaluated further. Principle component analyses were performed using Qlucore Omics Explorer 3. 1 (Qlucore, Lund, Sweden). Differentially affected pathways in 2Dsw and 3D spheroid culture were identified using overrepresentation enrichment analyses using WebGestalt  (Wang et al.
, Kyoto, Japan) connected to a SCIEX QTRAP 6500 mass spectrometer (SCIEX, Ontario, Canada) (for determining 4-OHtolbutamide and phenacetin) or a SCIEX API-4000 (SCIEX) (for determining 1-OH-midazolam, desethyl-amodiaquine, and dextrorphan) equipped with an ionization source operated in negative (4-OH-tolbutamide) or positive (desethyl-amodiaquine, 1-OH-midazolam, dextrorphan, acetaminophen) mode. Separation was carried out at 50 C using either a Waters Acquity C18 BEH 1. 7 mm-50 Â 2. 1 mm ID (Waters Corp. , MA-for midazolam and dextromethorphan) or a Waters Acquity HSS T3 1. 8 mm-2. 1 Â 100 mm (Waters Corp. , MA-for phenacetin, amodiaquine or tolbutamide). All data collection, processing, and analysis were performed with Analyst software (v1. 6. 2; SCIEX). The lower limit of quantification was set at 0.
Viability Measurements At 72 h (single treatment), day 7 (3 treatments), and day 14 (6 treatments), viability was determined by measuring cellular ATP using the CellTiter-Glo kit (Promega, Sweden or France). Blank values were subtracted from all wells. Curve-fitting was performed at a single site using the sigmoidal dose response function in GraphPad Prism, constrained at both 0 and 100. EC 50 values corresponding to the concentration of the compound causing a 50% reduction in viability were computed for each compound and time point. p-Values refer to heteroscedastic two-tailed t-tests unless otherwise stated. Values of p <. 05 were considered significant. For data to be included in the analysis, ATP values in control wells should not decrease by >50% over the 14 day culture period.
, 2009). This is consistent with our finding of increased APAP sensitivity in 3D spheroid versus 2Dsw culture after long-term exposure. Moreover, the 3D configuration was also more sensitive to diclofenac exposure, again in agreement with higher activities of CYP3A4 and CYP2C8 in 3D cultures, which are both implicated in diclofenac toxicity  (Bort et al. , 1999). Similarly, formation of reactive metabolites from troglitazone can occur through the activity of CYP3A4 and CYP2C8, which may explain increased sensitivity in 3D versus 2D in this study  (Yamamoto et al. , 2002; Yamazaki et al. , 1999). Nevertheless, all of these associations will need to be verified through pharmacological intervention, for example through 1-aminobenzotriazolemediated pan-CYP inhibition. In the case of fialuridine, the proteomic dataset does not support an obvious correlation of the expression levels of various proteins known to play a role in the uptake of fialuridine into the mitochondria and its metabolism to the active triphosphate moiety, such as ENT1, TK2, and TMPK, with differences in sensitivity.
ACKNOWLEDGMENTS We express our gratitude to  Prof. P. Bachellier  ( Hopital de Hautepierre ,  Avenue Molie  `re 67100 Strasbourg, France) and  Prof. B. Heyd  ( Hopital Jean Minjoz, 3 Boulevard Alexandre Fleming 25000 Besancon, France ) for providing liver samples. V. M. L. and M. I. -S. are co-founders and owners of HepaPredict, A. B. , C. C. B. , D. W. , S. R. , and A. J. F. are employed at  AstraZeneca , L. R. is a co-founder of KaLy Cell, L. R. and A. B. are employed at  KaLy Cell , S. J. and T. L. are employees of Orion OY, T. W. and C. S. are employed at  GSK , and A. W. M. v. d. V. , F. J. , J. v. H. , and J. S. are employed at  Janssen Pharmaceutical Companies of Johnson & Johnson.
Clinical data For each patient with acne, face and back acne scores were established by a dermatologist. These scores were estimated from 0 to 5 according to the type of acne (mild to severe), for both areas. All scores are shown in Table  I. Scores for back acne were established according to the Echelle de Cotation des Lésions d'Acné (ECLA) scale  (18). Scores for face acne were established according to the Groupe Expert Acné (GEA) scale  (19). The experimental scheme of the study is detailed in Fig. 1.
Clinical isolates and cultivable skin microbiota A total of 24 patients with acne and 12 healthy volunteers were recruited. For each patient with acne, GEA and ECLA scores were established by a dermatologist for the face and the back, respectively. Concerning the back zone, 15 of the 24 patients scored 3, 7 scored 4, and 2 scored 5. In addition, concerning the face zone, 14 scored 1, 8 scored 2 and 2 scored 3. The clinical data are shown in Table  I. Positive culture was obtained for all patients and controls (100%). Cultures identified P. acnes, but also other  ). However, no significant difference in bacteria diversity was noted on the back between acne and healthy individuals (p = 0. 820). In addition, no significant difference in bacteria diversity was noted between the face samples from subjects with acne and healthy subjects (p = 0.
1% of the face samples were CC18, 16. 6% were CC28, and 8. 3% were CC53 (phylotype II corresponding to K SLST-types). In addition, a significant association was found between SLST-type A1 and back acne skin-condition (p = 0. 002). All new SLST-types described for the first time in this study are as listed: A27 (acne back), A28 (acne face), F11 (acne face), K17 (acne back), K16 (healthy face and healthy back), and L7 (healthy back). These new SLST-types have been incremented in the online database, based on Christian F. P. Scholz's genetic system (http://medbac. dk/ slst/pacnes). The main results concerning identification of CC and SLST-types are summarized in Fig. 1.
Moreover, the results of the current study confirm those of Lomholt et al. (20)  regarding the predominance of IA1 phylotype (CC18) in patients with acne, but this study did not perform determination of SLST-types. In our study, among the large diversity of SLST-types found, we describe the significant association between A1 SLST-type and severe back acne. Overall, despite a highly conserved genome of P. acnes  (26) , we show that acne lesions are associated with the development of a specific subpopulation of P. acnes. From our 72 isolates, the A1 SLST-type was significantly associated with acne skin condition. In addition, we found 6 new SLST-types that had not been described previously (i. e. F11, L7, K16, K17, A28, and A27), which have been incremented in the online database (24) (http:// medbac.
Two hundred microlitre of erythrocyte pellets were homogenized in 10 volumes of TRIzol reagent (Invitrogen, Life Technologies, France) and stored at -80 °C until RNA extraction. Detailed characteristics of the study sites have been described  [24]. One hundred women with available clinical data throughout the follow-up and having parasitized samples (RDT+) (Rapid Diagnostic Test) at first antenatal visit (n = 50) or at delivery (n = 50) were included. Table  1  shows parameters for the two groups of women. All studies were approved by the Ethics Committee of the Faculty of Health Sciences, University of Abomey-Calavi (Benin).
Data analysis For each sample and each of the three gametocyte markers (Pf16, Pfs25 and Pfs230), the relative abundance was estimated relative to that of the P. falciparum seryl-tRNA synthetase gene from the same sample as delta CT values (ΔCT). ∆CT from qPCR reactions were calculated as the difference between the CT of the studied gene and the endogenous gene seryl-tRNA synthetase. The ratio of male/female gametocytes transcripts level at inclusion and delivery was calculated based on the relative copy numbers of Pfs25 and Pfs230 expressed as TU (transcript units). Samples corresponding to transcript levels with ΔCT value > 5 were assigned to ΔCT = 5 and therefore, TU was equal to 1  [32]. Unpaired t-test was used for comparisons between groups. All calculations were performed and data were analyzed with GraphPad Prism6 software (RITME Scientific Solutions, Paris, France).
Acknowledgements We are grateful to all the women who participated in the study and those involved in the  STOPPAM  project. The cohort study in Benin was part of the  STOPPAM  project, " Strategies To Prevent Pregnancy-Associated Malaria " (Contract Number: 200889). We thank all the medical staffs of Akodeha, Come Central and  Ouedeme Pedah  health centres, for their valuable contributions.