dataesr/scientific-paragraphs-categorization huggingface dataset:
LC-MS Profiles LC-MS profiles of crude extracts were acquired using a UHPLC-QTOF-MS equipped with an ESI source (QTOF-MS Xevo G2, Waters). Plant extracts were separated using a UPLC BEH C18 Acquity column (150 × 2. 1 mm i. d. , 1. 7 µm) with a gradient from 95% water (+0. 1% formic acid) to 95% CH3CN (+0. 1% formic acid) over 30 min at a flow rate of 460 µL/min. The QTOF-MS was operated both in NI and PI mode at a resolution of approximately 10,000 (full width half maximum). The data were acquired over an m/z range of 50-1000 in full scan mode. The capillary and cone voltages were set to 2. 5 kV and 40 V, respectively. The source temperature was maintained at 120 °C, and the desolvation and cone gas flows were set to 900 L/h at 350 °C and 20 L/h, respectively.
Acknowledgments This work was supported by the  Tunisian National Ministry of Higher Education and Scientific Research , University of Toulouse 3 and by the  French-Tunisian Hubert Curien Partnership "Utique " No. 12G0821. Marc Vedrenne  and  Nathalie Martins  from  "Institut de Chimie de Toulouse (ICT) " are greatly acknowledged for having recorded NMR and MS spectra, respectively. Finally, many thanks to  Peter Winterton  for the English revision.
One interesting thing described by Dziegielewska and coworkers is that calcium channels inhibitors could be therapeutically beneficial when combined with standard cytotoxic chemo-or radiotherapy. Thus already commercialized calcium inhibitors could be diverted to be used in chemotherapy. In this regard, clinical trial combining Mibefradil Dihydrochloride (a known antihypertensive drug) and Temozolomide has been approved for treating patients with recurrent glioma (NCT01480050)  [48]. Taken together, we found that calcium signaling regulates Mcl-1 through translational events and a calmodulinmediated pathway. BAPTA-AM and calmodulin inhibitor combination with ABT-737 leads to apoptosis, a process that is reversed by Mcl-1 enforced expression. As Mcl-1 represents a crucial hurdle to the success of chemotherapy, these results could open to new area of investigation using calcium modulators to directly or indirectly target Mcl-1 and thus efficiently sensitize ovarian carcinoma cells to anti-Bcl-x L strategies.
Investigation and validation of RGs The expression of five RGs was analyzed in this study to find the optimal candidate for normalization: ACTB, ALG9, GAPDH, PGK1 and RPL13A. When examined in previous studies on gene expression in alcoholics and smokers or in AMG and PFC, they showed no apparent regulation by the test conditions under examination or between brain areas  (Flatscher-Bader and Wilce, 2006 , MacKay et al. , 2011 , Ridge et al. , 2008 , Noriega et al. , 2010 , Boda et al. , 2009). Moreover, with the exception of GAPDH and PGK1, which are both involved in glycolysis, all other genes code for proteins with very different functions, minimizing the chances of having co-regulated RGs in the panel which would bias the ranking. Raw C q values were imported into the software packages GeNorm (used on the qbase PLUS platform, Biogazelle, Zwijnaarde, Belgium) and NormFinder (used on the GenEx platform, MultiD Analyses AB, Göteborg, Sweden).
Since the stability of reference transcripts as determined by geNorm and NormFinder can sometimes be in disagreement with the apparent inter-sample and inter-group variation as judged by relative quantification  ((Gebhardt et al. , 2010) ; M. Kubista, http://www. qpcrforum. com/, last accessed on 15 th December 2011), expression levels were also inspected manually. The geometric mean of GAPDH and ALG9 was also included in this assessment as a sixth candidate "gene" for normalization. Firstly, the fold changes for all the alcoholic and smoker cases combined were estimated over the NANS controls separately for each brain area (n = 12 and 4 for AMG for cases and NANS respectively; n = 14 and 5 for PFC)  (Livak and Schmittgen, 2001). Essentially, the average NANS C q value was subtracted from the value for each sample, and the resultant C q data were converted to fold changes, i.
e. 2 -Cq. C q values were compared between cases and NANS with an unpaired, two-tailed, Student's t-test. Similarly, the fold changes in expression between brain areas were evaluated by subtracting each PFC C q value from those of each AMG sample from the same individual (n = 13 for each brain area), and transforming this C q value to 2 -Cq. The 2 -Cq values from the two brain areas from the same individual were then compared with a paired, two-tailed, Student's t-test. Next, the inter-individual variability of each RG was estimated by calculating the fold changes over its average expression across all samples. This was calculated as 2 -Cq , where C q is the difference between the C q value of each sample and the average for each transcript  (Boda et al. , 2009). Finally, -C q values were used to examine any influence of RQI on RG expression with a two-tailed Pearson's correlation across all samples.
Characterization of primers and determination of PCR conditions BLASTn search of the primers specifically designed for this study (i. e. those for Adx, AdR and the RGs) showed 100% similarity to the target but no other genes. The specificity of each reaction and primer-dimer formation were also assessed by melting-curve analysis after each run. This revealed a single amplification product in all cases (data not shown), except for AdR, for which both primer sets showed little amplification across all brain samples. Primer-dimers were observed in some no-template controls, which otherwise showed no detectable signal. However, even when minor primer dimers were present in complete reactions, the technical replicate was excluded only if its levels were affected by the dimer formation, upon screening with Grubb's test.
Validation of RGs for normalization Details of the characterization of primers and validation of PCR conditions for qRT-PCR are presented in section 1. 1 of this Supplementary Information and in section 2. 5 of the main text, respectively. Expression levels differed for the five RGs genes, with GAPDH and ACTB having the lowest average C q values (19. 5  0. 9 and 20. 9  0. 9 respectively), i. e. being the most abundant transcripts, followed by RPL13A (23. 0  0. 5), PGK1 (23. 0  1. 0) and ALG9 (27. 5  0. 9) (Supplementary figure 4. The stability of candidate reference transcripts was assessed using three methods. Firstly, the geNorm algorithm ranked each transcript according to the average variation of its expression from that of each other candidate, with the lowest "M value" indicating the most stable gene.
All genes showed excellent stability (all M values  0. 5; supplementary table 5), with ALG9 ranked the best (M value = 0. 27), followed closely by GAPDH (M value = 0. 28) and ACTB (M value = 0. 29). The optimal number of RGs, to be used as their geometric mean, was computed to be two, namely ALG9 and GAPDH. Good agreement was found with the second algorithm, NormFinder when groups were compared with the two brain areas combined; again the top three genes were ALG9, ACTB and GAPDH (Supplementary table  5 ). When groups were combined to examine variations between brain areas, ALG9 was again ranked the best, followed by GAPDH and ACTB (Supplementary table  5 ). Group X Area comparison was not performed due to the small sample size. The comparable ranking of candidate RGs obtained with the two algorithms used here indicates that no coregulation of two transcripts occurred, since geNorm and NormFinder compare transcripts in a different manner (pair-wise vs globally).
The algorithms used by geNorm and NormFinder can sometimes return stability rankings different from those obtained by relative quantification of reference transcripts across samples and case groups  (Gebhardt et al. , 2010) ; Kubista, 2011). Expression variability was thus also evaluated by manual comparison of relative quantities between brain areas and case groups. All reference transcripts showed slightly higher expression in alcoholic and smoker cases compared to the controls, but this difference failed to reach statistical significance (Supplementary figure  5 ). After ranking each gene according to their fold changes over NANS (Supplementary table  6 ), an almost identical ranking was obtained in both brain regions, with RPL13A being the transcript showing the lowest variation (fold changes were 1.
2  0. 3 and 1. 4  0. 5 in AMG and PFC, respectively). When differences in expression between PFC and AMG were considered, the candidate gene showing the lowest fold changes was again RPL13A (1. 0  0. 3; supplementary figure  6 ). No statistically significant differences were seen in RPL13A expression levels, represented by 2 -Cq values, when the two brain areas from the same individual were compared (p = 0. 48, paired, two-tailed, Student's t-test). All other transcripts showed a significantly lower expression in AMG than in PFC, with average fold changes ranging from 0. 3  0. 2 for PGK1 to 0. 6  0. 3 for ACTB (all p values < 0. 001, paired, twotailed, Student's t-test; Supplementary figure  6 ). Finally, all genes showed slightly higher inter-individual variability in AMG than in PFC, with the CV ranging from 0.
4-1. 6 mL); injection site (abdomen or thigh); injection rate (0. 15 or 0. 45 mL/s) -in the tolerance of an SC injection of 0. 9% saline solution. A major factor influencing subject tolerance assessed using a 100 mm visual analog scale (VAS) was pain at the injection site (both statistically and clinically significant favoring abdomen over thigh). Pain intensity scores were statistically higher with larger volumes, but it was no different than scores for abdomen SC injections. Interestingly, injection rate did not play any role in subject's tolerance  21  as was seen in previous studies performed in house. However, based on available data today (published and unpublished), it has not been possible to determine the maximum volume and viscosity that can be delivered during a 10-15-second injection with an autoinjector and the impact of drug viscosity on subject tolerance.
All analyses were performed using the statistics software package SAS version 9. 1 (SAS Institute Inc. , Cary, NC, USA) by Eurofins OPTIMED Clinical Research (Gières, France). For all statistical tests, P-values lower than 0. 05 were considered statistically significant. All participants enrolled in the study completed the study and were included in the analysis population. The analysis was performed with all 144 performed injections. Among the 24 subjects, 12 experienced at least one injection system occlusion during the study, for a total of 22 recorded occlusions (Figure  1 ). Technical incidents appeared only with the 3 mL injections at the fast flow rate. These technical incidents appeared in 18. 2%, 27. 3%, and 50% of injections of the low-, medium-, and high-viscosity injections, respectively.
Demographic and clinical characteristics of participants Equal number of females and males (12 in each sex) were enrolled in this study (Figure  1 ), with ages ranging from 35 to 50 years. The weight and height ranged from 48. 9 to 99. 7 kg and 150 to 195 cm, respectively. The BMI ranged from 20. 1 to 28. 4 kg/m². General demographic data are summarized in Table  1. No significant differences in demographic parameters were found between the subjects receiving either 2 or 3 mL injections. No history of chronic disease or abnormal abdominal skin condition was recorded for enrolled subjects; they were all assessed as healthy. Each participant received six SC injections in the abdomen as depicted in Figure  2.
Pharmacokinetic study Male SpragueeDawley rats were orally administered ginseng extract or vehicle after receiving control or NUTRIOSE-containing diets (control diet þ NUTRIOSE) for 2 wk. Blood samples (200 mL) were collected from the tail vein at 0 h, 1 h, 2 h, 4 h, 8 h, 12 h, 16 h, 20 h, and 24 h after administration of ginseng extract and centrifuged for 10 min at 4,000 Â g to prepare the plasma samples. The samples were analyzed using liquid chromatographyetandem mass spectrometry analyses according to a previously reported method  [18]. Plasma concentration data for individual rats were analyzed with a noncompartmental method by using WinNonlin Professional 3. 1 software (Scientific Consulting, Inc. , Lexington, KY, USA). The area under the plasma concentrationetime curve (AUC) was calculated by using the trapezoidal rule.
Effect of NUTRIOSE on glycosidase activities of intestinal microbiota Ginsenosides have different sugar moieties such as glucose, rhamnose, and xylose. Thus, biotransformation of ginsenosides to CK involves glycosidic bond cleavage by gut microbial hydrolysis. To investigate the effects of NUTRIOSE on ginsenoside-metabolizing enzyme activities of intestinal microbiota, the glucosidase activities of intestinal contents were measured in rats treated with NUTRIOSE for 2 wk (the intestinal contents were collected from the rats used for the pharmacokinetic study described above). As shown in Fig. 4 , a/b-D-glucosidase activities were increased by NUTRIOSE treatment. This result is accordance with previously reported data  [14]. a-L-Rhamnosidase activity was also increased by treatment with NUTRIOSE.
Effect of NUTRIOSE on CK formation by intestinal microbiota To investigate the effects of NUTRIOSE on CK formation by gut bacterial metabolism, CK-forming enzyme activities of rat intestinal contents were measured following NUTRIOSE treatment. Ginsenoside Rb1 was used as a substrate to determine CK-forming enzyme activities. Ginsenoside Rb1 is a representative protopanaxadiol ginsenoside and the most abundant ingredient of ginseng extract, which is extensively metabolized by gut microflora to yield CK. The resulting data showed biotransformation of ginsenoside Rb1 to CK by fecal bacteria increased with increasing NUTRIOSE content (Fig. 5A ). The metabolic rates in the 5% and 10%   NUTRIOSE-treated groups (289 AE 84 nmol/h/g and 311 AE 86 nmol/ h/g, respectively) were >10 times higher than that in the control group (26 AE 14 nmol/h/g).
Methods derived from big data and natural language processing (NLP) need to be considered. Recent works have proposed solutions to address these issues and to standardize the process of extracting information from Web pages in social media. In addition, questions remain about the quality of information available in users' Web 2. 0 discussions. Whereas electronic health records and health professionals' ADR reports are structured and well documented, there are no requirements regarding writing and structuring descriptions of pharmacovigilance-related events on social media, and information may be scarce or incomplete. We performed a scoping review of relevant previously published studies to assess how social media can be used as a data source for postmarketing drug surveillance. This type of literature review aims at providing an overview of the type, extent, and quantity of research available on this topic.
Stage 2: Identifying Relevant Studies Two electronic databases-PubMed and Embase-were searched for English and French articles. The PubMed database was searched twice, as follows: 1. With the following keywords (query #1) to investigate the pharmacovigilance and social media dimensions: pharmacovigilance, adverse reaction, adverse event (AE), drug, medication, pharmaceutical product, social media, Web 2. 0, social network, Twitter, Facebook, blog, forum, fora, message board, comment, and user feedback. An outline view of this request is presented in Figure  1. 2. Medical Subject Heading (MeSH) terms (query #2)-pharmacovigilance, natural language processing, Adverse Drug Reaction Reporting Systems, and Internet-associated with the following keywords in the title or the abstract: surveillance, Twitter, Facebook, Doctissimo (the main French health-related discussion forum), social media, social network, online health community, online discussion, medical data mining, online, patient forum, and natural language processing.
Query #3 was specially designed for the Embase database based on query #1. The details of the three queries are given in Table  1. The upper date limit of June 2014 was applied, with no lower date limit, considering that articles published in the early days of social networks could be of interest. As an iterative process and in accordance with the scoping review methodology, all references from the studies selected in stage 3 were screened, as were all of the publications that cited the selected studies. To broaden the scope of the search, Google Scholar was also used to search for citations. "pharmacovigilance"/de OR ADR OR ADE OR ("adverse reaction"/de OR "adverse event" OR "side effect"/de AND ("drug"/de OR "medication"/de OR "pharmaceutical product")) AND ("social media"/de OR "Web 2. 0":ab,ti e OR "Web 2.
Not related to ADR reporting or ADR detection (eg, efficacy or effectiveness of a study's design). 3. Not related to patients' reporting (eg, a safety study in animals, signal detection on a pharmacovigilance database). 4. No, or insufficient, results on the use of social media. 5. The study was a review. 6. Soliciting reporting: the study used data from a source where patients were asked to report the ADRs. Patients' behavior is different depending on whether they are asked to report ADRs or they describe ADRs spontaneously without knowing that there may be further analysis of what they write  [28]. 7. Editorial: the study did not encompass a result but was an expression of the author's opinion about the usefulness of social media as a new source of knowledge for pharmacovigilance.
Description Step This step consists of identifying and selecting the most relevant websites to answer the research question. They can be identified using a combination of keywords (eg, generic or brand-name drug, disease, ADR a /AE b ) in Web search engines. Step 0: Selection of data sources Potentially relevant patient narratives or posts are identified by entering keywords into the search engine hosted by the selected websites (manual identification only) or using a semiautomated process. Data may be imported into software (after anonymization) with the aim of additional analyses. Step 1: Data collection The manual identification of drug-ADR/AE pairs is performed by reading the patients' narratives or posts that were initially collected. Step 2: Identification of drug-ADR/AE pairs This step consists of manually evaluating the frequency and the seriousness of the ADRs or AEs that were identified in patients' narratives or posts.
In 6 of 11 (55%) studies, the authors verified if the ADRs that were identified in social media were expected or not and compared them with those that had been reported in clinical trials (3/6, 50%)  [33, 36, 37] , in pharmacovigilance databases (2/6, 33%)  [30, 35] , or in the studied drugs' Summary of Product Characteristics (SPC) (1/6, 17%)  [31]. Out of 11 studies, 2 (18%) reported using a standard terminology-Medical Dictionary for Regulatory Activities (MedDRA)  [30] -or Problem Intervention Documentation (PI-Doc)  [36] , a German classification system, for coding the ADRs or AEs that were reported in social media. Freifeld et al  [30]  found that the AEs/ADRs identified in social media had similar profiles to those that were spontaneously reported through official channels, whereas Pages et al described the qualitative differences between the data sources  [35].
The pharmacovigilance database that was used for comparison was the FAERS  [54]  in 4 of the 13 (31%) studies. In 7 other studies out of 13 (54%), the annotators had varying expertise levels-from medical school students to pediatric clinicians and those with PhDs. Benton  [38]  referred to the tables and notes contained on the drug labels. Overall, only Li  [43]  did not document the constitution of a new gold standard or the use of an existing standard. A majority of studies (7/13, 54%) were not only about expected ADRs, but also about discovering relationships between drugs and adverse events that had not been documented on the drug labels or in the literature.
Finally, we retrieved no study that took into account exposures during pregnancy and only one study that partly focused on drug-drug or food-drug interactions. More than the quality of the information shared in social media, issues can be raised about the reliability of this information. Indeed, social media users adopt pseudonyms, which may allow malicious persons to spread false rumors using multiple pseudonyms with limited risk of being identified as the origin of the rumor. Furthermore, a user can post the same message twice or more on the same forum or on different forums using the same or different pseudonyms with no malevolent intent simply to maximize their chances of obtaining an answer. Consequently, it would be interesting to identify these duplicates. We found only one study in which an algorithm that addressed data redundancy was implemented but not described  [40] , and removing duplicates was seldom reported as an issue, for example, in Pages et al  [35].
Annotation and use of existing medical terminology. Because none of the selected articles reported on a method that encompassed all of the steps we considered key, we assume that refining current methods and tools is desirable to improve the quality of processed data. We also noticed that implementation did not follow a generic approach, which would be necessary for easily adding new sites or data sources. This is understandable in the context of a research project, but genericity should be addressed if more sites are intended to be included in the general pharmacovigilance process. Finally, from the studies returned by our citation database queries, no study used comments on video-sharing websites as a source of data.
Moreover, extracting ADRs from social media presents specific technical constraints, given the unstructured information, compared with electronic health records or pharmacovigilance databases. Finally, spelling errors and patients' expressions  [60, 61]  make extraction even more difficult. However, our study confirms that there is a sufficient volume of data on pharmacovigilance in social media to work with and that quantity may eventually support the pharmacovigilance process despite variable quality. Whereas some websites may collect huge amounts of poorly documented posts, others such as PatientsLikeMe  [60]  collect very complete and high-quality data on drug treatments and, therefore, present very interesting possibilities for improving our knowledge on ADRs based on reliable information.
Language used in the studied texts ✓ ✓ Type of data source, for example, forums or Twitter Keywords the authors used to identify sources or posts of interest ✓ Use of semiautomated processes (mixed methods) ✓ Main results ✓ Whether reported ADRs were highly informative or not ✓ Seriousness of reported ADRs ✓ Reference source was used for comparison with reported ADRs ✓ Identification of potential unexpected ADRs or unexpected frequency of known ADRs ✓ Analysis of the influence of other media, for example, television, radio, or the press, as a potential cause of increased ADR reporting in social media ✓ If the authors mentioned the use of a crawler ✓ Implemented methods of preprocessing ✓ Lay language lexicon or tools used ✓ Authors attempted to identify the relationship between the drug and the event ✓ Authors used a machine-learning approach ✓ Evaluation of the extraction methods with metrics ✓ Comparison with external pharmacovigilance databases ✓ Whether the system enabled evaluating the unexpectedness of any extracted ADRs a ADR: adverse drug reactions.
Acknowledgments This work was funded by the grant  AAP-2013-052  from the  French agency for drug safety ,  Agence Nationale de Sécurité du Médicament et des Produits de Santé (ANSM) , through the  Vigi4MED  project, and by the  Direction Générale des Entreprises (DGE)  and territorial collectivities ( Ile de France  and  Haute Normandie)  under the 16th  Fond Unique Interministériel (FUI)  request for proposals through the ADR- PRISM  project.