dataesr/scientific-paragraphs-categorization huggingface dataset:
In silico analysis of extracts Bio-guided fractionation is a conservative method used to isolate potentially active components, however it is time-consuming. Substantial work has already been carried out on many different families and genera of plants, subsequently relatively complete databases are now available. Thanks to the development of bioinformatics, it is now possible to search for potential active metabolites using databases containing compounds previously isolated. This is a faster approach with relatively low uncertainty. As seen in Table  1 , we identified 28 metabolites in FACA V R and DREPANOSTAT V R that had scoring values above 7. 5 and were in agreement between the screened databases. In total, 520 integration peaks were realized for DREPANOSTAT V R in the positive and negative ionization modes.
Among them, 270 were identified in MS-FINDER using the plant databases, but only 64 of them had a scoring value above 7. 5. Finally, only 19 of them were in agreement with our in-house databases. For FACA V R , 467 integration peaks were realized with MS-DIAL in the positive and negative ionization modes. Among them, 284 were identified in the plant databases and 55 of them were retained with a scoring value above 7. 5. Only 16 of them agreed with our in-house databases. This low number of identified metabolites is due to our strict filtering method, selecting only those metabolites with high scoring values and that were in agreement between databases. As a result, the identification of false positives was strongly reduced. A few of the metabolites presented in Table  1  have previously been isolated and identified  (Carboni 2009; Chaaib Kouri 2004) , from the same family of plants but sometimes from different genera.
The study has been declared to the French Data Protection Authority (Commission Nationale Informatique et Libertes, CNIL, identification no. 1980063), as required in France. No formal consent was required for this type of study according to French law. Physicians were invited to participate after receiving information about the study on the home page of the study website, and were able to download the study protocol via a dedicated link. Participation by logging into the platform was voluntary and anonymous. Crowdsourcing procedure. Eligible physicians were invited to log into an Internet platform (http://clinicalepidemio. fr/mapp/), where they provided basic demographic information (age and sex) and professional information (medical specialty, ambulatory or hospital practice setting). Then, they assessed as many clinical vignettes as they wanted (they were allowed to skip clinical vignettes for which they did not feel comfortable to assess the TURN).
For each drug-indication group, we computed the number of pill boxes reimbursed in 2016 according to the French national health insurance medication database by summing the number of pill boxes for all medications belonging to the given group. We presented our results in a figure that allowed for comparing prescription volumes among the different drug-indication groups and their respective summary TURN: each drug-indication group was presented as a rectangle with color and area size by TURN and prescription volume, respectively (Fig  2).
Estimating the proportion of prescription volume for each drug-indication group Fig 2 plots all drug-indication groups with their corresponding summary TURN (rectangle color) and prescription volume (rectangle area size). Overall, 21. 0% (in proportion of reimbursed pill boxes) of the medications assessed corresponded to drugs with the lowest TURN (i. e. , risk of non-adherence considered unacceptable with 1 daily dose missed per month). Similarly, 44. 9% of the medications assessed corresponded to drugs with moderate or high TURN (i. e. , risk of non-adherence considered unacceptable with more than 1 missed daily dose per week). Each drug-indication group is plotted as a rectangle: 1) rectangle color corresponds to the TURN � ; 2) rectangle area size is proportional to the number of pill boxes reimbursed in 2016 in France (according to the French national health insurance database).
For diffusion coefficient measurements, 2D 1 H DOSY NMR experiments were carried out using the Bruker sequence ledbpgp2s. Gradient calibration of the probe was done using the water signal from an H 2 O/D 2 O (90/10) mixture; the gradient value G = 4. 9 G/mm was obtained for a water diffusion value D at 2. 3 × 10 -9 m 2 /s at 25 • C. The strength of the pulsed-field gradient was linearly increased from 2 to 95% in 16 steps. The diffusion time (∆) and the gradient duration (δ/2) were set at 80 ms and 1. 5 ms, respectively. The longitudinal eddy current delay and the spoil gradient delay were fixed at 5 and 0. 2 ms, respectively. Spectral data were processed via the dosy2d module from TOPSPIN software (version 3. 2, Bruker Biospin, Wissembourg, France). Fourier transform was applied in the F2 dimension for every FID obtained from each programmed gradient value and a baseline correction was applied on every one-dimensional spectrum.
Solubility Studies Phase solubility studies were carried out according to the modified Higuchi and Connors method. Excess amount of MOR were added (20 mg) to a 0. 6 mL D 2 O solution containing different α-CD concentrations (from 10 to 50 mM). The suspensions were maintained at 300 K for 24 h, filtered through 0. 2 µm cellulose filters from Grace (Columbia, MD, USA) and analysed by a 1 H-NMR quantitative experiment called ERETIC  [24]. First, a 1 H-NMR experiment using zg pulse program (D1 5 s, AQ 4. 54 s, TD 64K, NS 8) with a Bruker reference standard triphenylphosphate (TPP) (48. 5 mM in aceton-d6)was performed. This reference spectrum obtained for TPP with a known and accurate concentration enabled to assign the integrated signals to 15 protons and was used to externally calibrate and quantify MOR at saturation in the absence and in the presence of α-CD in D 2 O.
The HMRS spectra were recorded in the centroid mode. For MS/MS experiments, argon was used as collision gas and the collision energy was optimized for each [M + Na] + precursor ion. For IM-MS experiments, separation of ions in gas phase was achieved in the travelling wave ion mobility cell using nitrogen as drift gas and the following wave velocity parameters: (WV) = 600 m/s, wave height (WH) = 40 V, to obtain mobility traces of each sample. Data acquisition was performed with MassLynx software (V4. 1, Waters).
Modelization Studies Molecular dynamics were performed using the OPLS force field, with an implicit simulation of water (Generalized Born model, dielectric permittivity equal to 78), as implemented in the Abalone software. The CD host was based on a non-distorted α-CD structure with C6 symmetry, while MOR was constructed by means of the tools integrated in the software. MOR was then manually introduced within the α-CD cavity, with a careful attention devoted to the observed steric complementarity. An initial optimization step of the inclusion compound was realized (steepest descent algorithm), before launching molecular dynamics simulations (NPT ensemble at constant pressure of 1 atm and temperature of 298 K, integration step 1 fs, equilibration time 1 ns, simulation time 10 ns). All generated structures were used to calculate the interaction energy (sum of the van der Waals and electrostatic interactions between the two partners within the complex) and the complexation energy (difference between the total energies obtained for the complex and for the sum of the isolated species).
Classical methods used for signal detection in pharmacovigilance rely on disproportionality analysis of counts aggregating spontaneous reports of a given adverse drug reaction. In recent years, alternative methods have been proposed to analyze individual spontaneous reports such as penalized multiple logistic regression approaches. These approaches address some well-known biases resulting from disproportionality methods. However, while penalization accounts for computational constraints due to high-dimensional data, it raises the issue of determining the regularization parameter and eventually that of an error-controlling decision rule. We present a new automated signal detection strategy for pharmacovigilance systems, based on propensity scores (PS) in high dimension. PSs are increasingly used to assess a given association with high-dimensional observational healthcare databases in accounting for confusion bias.
Performance was assessed through an empirical comparative study conducted on a reference signal set in the French national pharmacovigilance database (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016) that was recently proposed for drug-induced liver injury. Multiple regression approaches performed better in detecting true positives and false positives. Nonetheless, the performances of the PS-based methods using Courtois et al. Propensity Score Approaches in Signal Detection matching weights was very similar to that of multiple regression and better than with the univariate approach. In addition to being able to control FDR statistical errors, the proposed PS-based strategy is an interesting alternative to multiple regression approaches.
INTRODUCTION Once a drug is introduced on the market, many people are exposed to it in real-life conditions, which can be very different from those evaluated in clinical trials. The goal of pharmacovigilance is to detect as quickly as possible potential adverse reactions which could be induced by drug exposure. To achieve this challenging task, health authorities collect and monitor spontaneous reports of suspected adverse events (AEs), mainly from practitioners. In France, such a pharmacovigilance database is maintained by the National Agency for the Safety of Drugs and Health Products (Agence Nationale de Sécurité du Médicament et des Produits de Santé, ANSM). It contained around 431,000 reports at the end of July 2016. In recent years, about 36,000 reports have been reported annually. To exploit this amount of data, several automated signal detection tools have been developed.
e. , one drug, one AE) to deal with confounding like indication bias. In recent years, this methodology has been used with high-dimensional health-care databases, like claims data, by collapsing all the numerous covariates present in these databases into a single value which is easier to manipulate  (Seeger et al. , 2005; Schneeweiss et al. , 2009). Tatonetti et al. (2012)  investigated the use of one PS strategy in the context of spontaneous reporting data and illustrated its interest by comparing it to a disproportionality method. In this paper, we develop several PS-based approaches for signal detection from spontaneous reporting and compare them to penalized multiple regression approaches. We built the PSs with four different algorithms adapted to high dimensional settings and for each of them, we considered three different integration strategies to perform signal detection.
Reference Methods The most common proposed methods for signal detection are disproportionality methods, which rely on the aggregated form of the data. These methods aim to detect higher-than-expected combinations of drugs and events in the database. Nevertheless, two kinds of biases are created by the univariate feature of disproportionality methods: masking and the co-reporting confounding effect  (Harpaz et al. , 2012). In order to address these two bias issues, other approaches in pharmacovigilance have lately emerged that rely on multiple logistic regressions  (Caster et al. , 2010). Individual spontaneous reports are directly analyzed instead of aggregated counts of drug-event combinations. We denote by I the total number of drugs and J the total number of AEs in the database. Let Y j indicate the presence or absence of AE j , and X i denote the presence or absence of drug i.
The corresponding logistic model for each AE j is logit(Pr(Y j = 1)) = β j 0 + I i =1 β j i X i. (1) In spontaneous reporting databases, the number of drug covariates I is very large. To deal with this high-dimensional issue and in order to derive the most parsimonious model, a penalized lasso logistic regression was implemented  (Tibshirani, 1996). This consists in maximizing the log-likelihood of model (1) minus a penalization term pen j (λ) = λ |β j | 1 = λ I i =1 |β j i |. (2) Depending on the penalization parameter λ, the lasso regression shrinks most of the coefficients associated to the covariates to exactly zero, so these covariates are not retained in the model. Usually cross validation is used to select the best value of λ in terms of prediction error, but this does not achieve good performance in a variable selection framework which is that of signal detection.
CISL Another way to get around the penalization parameter selection issue is the stability selection method  (Meinshausen and Bühlmann, 2010). To take into account the sparsity of pharmacovigilance databases with low frequencies of the outcomes,  Ahmed et al. (2018)  proposed a variation of this method: the CISL algorithm. In this method, samples are drawn following an nonequiprobable sampling scheme with replacement to allow a better representation of individuals who experienced the outcome of interest. For a given set of penalization parameter values, lasso logistic regressions are computed in each of these samples. The CISL procedure consists in computing the quantity: π b i = 1 E E η =1 1[ β η,b i > 0], ( 4 ) where E is the maximum value of covariates selected by all the lasso regressions, η ∈ {1,.
Propensity Score Modeling It is recommended to include predictors and confounders in the PS model i. e. , covariates that are related to the outcome or to the outcome and the exposure. It is also strongly advised to avoid instrumental variables i. e. , variables that are only related to the exposure  (Patorno et al. , 2013). In practice, it is sometimes recommended to make a priori variable selection according to expert knowledge  (Brookhart et al. , 2006). In the highdimensional framework such as health-care databases where thousands of covariates are entered, this approach cannot hold. The high-dimensional propensity score (hdPS) algorithm was proposed to deal with this kind of difficulty  (Schneeweiss et al. , 2009). For a given exposure, the choice of candidate covariates to include in the PS model relies on empirical assessment of covariates prevalence and strength of association with the exposure and with the outcome of interest.
The algorithm ranks all input variables by their potential for confounding and the investigator must choose the top-ranked variables to be included in the model for estimating the PS. In the present work we aimed at computing the PS for each drug in the database by selecting among all the other drugs those to be included in the PS estimation model. We implemented four PS-estimation methods. We considered three variable selection algorithms: hdPS and the two other variable selection methods described above: the BIC-Lasso and the CISL [with the positive constraint for β replaced by a non-zero constraint in Equation (  4 )]. Using the BIC-lasso methodology, each covariate associated with the smallest BIC models was selected. Using CISL, all covariates with a 10% percentile of its distribution greater than zero were selected.
, 2002). We applied the same FDR controlling procedure to this univariate approach. All the analyses were performed with R version 3. 4. 0 (R Core Team, 2017). All the logistic regressions were computed with the speedglm R package v0. 3-2. All lasso regressions were implemented using the glmnet R package v2. 0-10. GTB algorithms for PS estimation were implemented with the xgb. train function from the xgboost R package 0. 6-4. The default values of the xgb. train function were used, except for the learning rate, which was fixed at 0. 1. Table  1  summarizes the main advantages and disadvantages of the different signal detection methods presented in this section. For the sake of clarity, we refer to the multiple regression approaches in the following as BIC-Lasso, CISL-5%, CISL-10% and to the univariate approach as Univ.
The French Pharmacovigilance Database The performances of our approaches were evaluated and compared by an empirical analysis using data from the French pharmacovigilance database. Drugs are coded according to the 5th level of the Anatomical Therapeutic Chemical (ATC) hierarchy, and the AEs to the Preferred Term (PT) level of the Medical Dictionary for Regulatory Activities (MedDRA). We applied some filters to the data and considered (i) only products which are known to be a drug (leaving out vaccines, phytotherapy, homeotherapy, dietary supplement, oligotherapy and enzyme inhibitor); (ii) reactions which are targeted as AEs (overdoses or medication errors are not taken into account). Drugs are listed according to their active substances, which are encoded with the ATC classification and additional codes for substances that do not have a corresponding code.
Only drugs confirmed as DILI cause were retained  (Chen et al. , 2016). We translated DILI labeling keywords into PT codes from the MedDRA classification: 91 PT codes were considered as DILI related. Each spontaneous report involving one of these 91 PT was thus considered as a reported DILI event, which translated into 22,440 DILI reports in the French pharmacovigilance database. To avoid numerical issues due to very low drug-reporting frequency, we limited the comparison to drugs which have more than three reports in common with a DILI, and have more than ten reports in total. This restriction applied, we considered 922 different drugs out of the 2,344 original drugs from the spontaneous reporting database. Considering these drugs, the final DILIrank set accounted for 417 (drug, DILI) pairs: 90 no DILI concern pairs, 213 less DILI concern pairs and 114 most DILI concern pairs.
DISCUSSION Development of novel signal detection methods is crucial to improve the responsiveness and the efficiency of post-marketing surveillance systems. The main issue is to develop techniques that give a reasonable number of signals for further analysis by experts, while providing a list of relevant signals with the fewest possible false associations. Using spontaneous reporting databases is difficult because of their high dimension and extreme imbalance. This study compared recently proposed multiple approaches and a set of methods based on the PS in a signal detection framework. To do so, we used a recently developed set of reference signals pertaining to liver injury: the DILIrank set. The best performing PS-based approach is the one which relies on the MW. Its performance in terms of true/false signal detection, specificity and sensitivity is very close to that of the multiple regression approaches, which performs the best.
On the contrary, the three other PS-estimation methods have a computational advantage: once the PS is estimated for a given drug, it can be used to test associations with any AEs. When PS is estimated with the covariates selected by BIC-Lasso, CISL or with a GTB algorithm, PS-based approaches are competitive with multiple regression approaches in terms of calculation time. Regressions like weighted univariate logistic regressions can be easily performed. Depending on the PS methodology used (adjustment or weighting), results are roughly comparable according to the way PS is estimated. A major constraint in developing signal detection methods is to obtain a reliable set of reference signals to assess performances. Here we used a recently established set, the DILIrank set pertaining to a common adverse event, which is not the case for all the AEs in the database.
Our group previously showed that the anti-apoptotic proteins Mcl-1 and Bcl-x L were able to cooperate to allow ovarian carcinoma cells to overcome apoptosis, as their concomitant inhibition was sufficient to induce cell death  [5] [6] [7]. These proteins are now considered as relevant targets for the treatment of chemoresistant ovarian cancers, and the identification of adequate strategies for their efficient inhibition constitutes an exciting challenge. The crucial role of Bcl-x L in cancers has led to the development of many tools to counteract its anti-apoptotic effect such as BH3-mimetics. These molecules, which mimic the BH3 domain of pro-apoptotic proteins, can hinder the function of anti-apoptotic proteins by binding their hydrophobic groove. Among these molecules, ABT-263 (Navitoclax), the orally administrable derivative of ABT-737, is currently undergoing phase I and II clinical trials in different locations including ovarian cancer (NCT02591095).
Moreover, combining CAI with siRNA targeting Bcl-x L triggered massive apoptosis in the three cell lines, as suggested by caspase 3 and PARP cleavages (Supplementary Data 3). We then evaluated the efficacy of the YM58483/ ABT-737 combination in our carcinoma cell lines. Whereas YM58483 did not induce any cell death on its own at the concentrations tested, its combination with ABT-737 elicited a massive apoptosis, as displayed by the emergence of a strong sub-G1 peak and caspase 3 and PARP cleavages (Figure  5D  and 5E ). Altogether, these experiments illustrate that inhibiting SOCE strongly sensitizes chemoresistant ovarian cells to anti-Bcl-x L strategies.
Subsequent replenishment of 2 mM Ca 2+ to the medium elicited a rise in [Ca 2+ ] i due to Ca 2+ influx through open store-operated channels. Black tracings depict the representative changes in [Ca 2+ ] i recorded from DMSO treated cells and grey tracings depict the representative changes in [Ca 2+ ] i recorded from cells pre-treated 1h with 15 μM for IGROV1-R10 and OVCAR3 cells or 20 μM for SKOV3 cells (data are representative of three independent experiments). (B) Effect of YM58483 on Akt/mTORC1 pathway. Cells were treated with YM58483 (15 μM for 48h for IGROV1-R10 cells, 15 μM for 72h for OVCAR3 cells and 20 μM for 72h for SKOV3 cells). The effect of YM58483 treatment on the activation of the PI3K/Akt/mTOR pathway was analyzed by studying the protein expression of P-Akt (Ser473 and Thr308) and total Akt, P-mTORC1 (Ser2448) and P-mTORC2 (Ser2448), P-p70S6K (Thr389) and total p70S6K and P-4E-BP1 (Thr70) and total 4E-BP1 by western blot analysis.
Protein levels (standardized based on actin) were determined by densitometry scanning with Image J software to generate the values shown in the bar graphs. Results are expressed as mean ± SEM. Statistical differences were analyzed with a Student t-test: * p< 0. 05, ** p< 0. 01, *** p< 0. 001 (n=3). www. oncotarget. com sensitizes ovarian carcinoma cells to ABT-737. Cells were treated with YM58483 (15 μM for 48h for IGROV1-R10 cells, 15 μM for 72h for OVCAR3 cells and 20 μM for 72h for SKOV3 cells). One micromolar ABT-737 was added for the last 24h hours of treatment. Sub-G1 events were studied by flow cytometry. (E) YM58483 sensitizes ovarian carcinoma cells to ABT-737. Cells were treated with YM58483 (15 μM for 48h for IGROV1-R10 cells, 15 μM for 72h for OVCAR3 cells and 20 μM for 72h for SKOV3 cells).
Cell culture The human platinum-resistant ovarian carcinoma cell lines IGROV1-R10, OVCAR3, and SKOV3 were used. IGROV1-R10 was established as described previously  (34)  from the IGROV1 cell line, kindly provided by Dr. Jean Bénard (Institut Gustave Roussy, Villejuif, France). OVCAR3 and SKOV3 were obtained from the ATCC. The cell lines were authenticated in April 2016 by Microsynth who compared their STR profiles with the ATCC database. They were grown in RPMI1640 medium (Gibco) supplemented with 2 mM Glutamax TM , 25 mM HEPES, 10% FBS (Gibco) and 33 mM sodium bicarbonate and were maintained in a 5% CO 2 humidified atmosphere at 37°C.