dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion Medication analyses by a pharmacist help to prevent adverse drug reactions. However, the data given to pharmacists by several databases are rarely accurate enough to allow drug therapy to be safely modified in cases of a DDI, cirrhosis, or the presence of gene polymorphism. Drug-drug interactions with a true clinical impact are not frequent  [21]  but require pharmacists to be vigilant. [22]  In the present study, 92 of the 196 of the DDI-Predictor's alerts (i. e. , more than 40%) were considered to be significant (R AUC ≤ 0. 5 or >2) by the clinical pharmacist. More than 80% of the alerts results in a PI. Cases without a PI concern situations with limited clinical impact, a slight change in the AUC, the presence of several interacting drugs in the same prescription, or another cause of pharmacokinetic alterations (e.
g. , obesity or kidney failure). These complex cases will prompt the pharmacist and the physician monitor the patient closely regarding to the difficulty to advise any PI. In our department, the pharmacist first analyses DDIs by reference to the ANSM thesaurus. The GUH table was published (and thus implemented) before DDI-Predictor. Our results show that the three tools are complementary-partly because they have different objectives. The ANSM thesaurus lists DDIs considered to be clinically significant  [15]. The GUH table summarizes in vivo and in vitro data on potential biotransformation pathways or drug interactions  [16]. Rapidly analyzable data improve the pharmacist's analysis. DDI-Predictor's output (R AUC ) is calculated using physiologically based pharmacokinetic based models, which gives the pharmacist and idea of the "strength" of a DDI.
Clinical decision support systems alert pharmacists and physicians to adverse drug reactions and medication errors  [23]. Pharmacists can use these tools to define rules for the detection of pharmacokinetic changes (including DDIs), issue PIs, and thus improve patient safety  [24]. Additional data from DDI-Predictor could be usefully included in this process. Nonetheless, the use of DDI-Predictor has several limitations. Firstly, its use depends on the pharmacist's level of knowledge of the interacting agents. Most of the recorded interactions involved very frequently prescribed drugs, and many are also well-known interactors: carbamazepine  [25] , rifampicin  [26] , paroxetine  [27, 28] , fluoxetine  [27] , and fluconazole  [29]. It is crucial to monitor co-prescribed drugs when judging the significance of interactions.
The fact that the pharmacists were very aware of these drugs' interactor potential might explain (at least in part) their high frequency in the database, relative to drugs with lesser known interacting effects. Indeed, some DDIs might have been omitted due to a lack of awareness, especially if the drugs are now frequently prescribed. This is why reviewing PIs can also help raise awareness of DDIs among pharmacists. Although the lists of drugs change continuously, the application is limited in the testable cases. Thus, DDI-Predictor cannot analyze all DDIs. A high proportion of the initially detected alerts (42 out of 284 (14. 7%)) could not be classified. This might be due to (i) the lack of published data in humans or (ii) drug metabolism by enzymes other than CYPs (e. g. , the production of glucuronated or sulfonated metabolites or substrates for P-glycoprotein or organic anion transporters (e.
g. , OATP2B1))  [28]. Further development of DDI-Predictor might resolve these issues. DDI-Predictor's algorithm computes R AUC on the basis of linear kinetics, which is a poor approximation for drugs like phenytoin and voriconazole  [29]. DDI-Predictor might complement other databases and thus improve DDI screening. In the present study, DDI-Predictor was applied to a small proportion of the analyzed prescriptions. New tools have a learning curve. Some data may not be recorded, and some interactions may not be detected by pharmacists; this is also a study limitation. The underuse of DDI-Predictor can be also explained by the fact that some detected DDIs involved well tolerated home medications. In the absence of poor efficacy or toxicity, PIs were not issued. Lastly, some misuses of DDI-Predictor were identified.
Data Collection Data were continuously acquired during routine computer-assisted drug prescription for inpatients in our institution. After being anonymized, the data were recorded in a standardized Excel ® spreadsheet (Microsoft Corporation, Redmond, WA, USA). The data analyzed in the present study were generated over 24 consecutive months. The department's pharmacists were trained by two expert pharmacists (FM and NS), who had initially been trained by the DDI-Predictor's developers. Each pharmacist was asked to present a clinical case involving DDI-Predictor, in order to check that the application was being used correctly. Moreover, quality assurance documents (for correct use of DDI-Predictor and help in interpretating the results) were created at the start of the study. After using DDI-Predictor, the pharmacist filled out a care report form.
Data Coding Each tool categorizes the strength or risk level of each detected DDI. According to the ANSM thesaurus, the strength/risk level of a DDI is categorized as 1: a contraindicated drug combination; 2: an at-risk drug combination; 3: a drug combination to be administered with caution; or 4: an interaction to be taken into consideration The GUH tables classify DDIs into 5 groups, depending on the combination of weak or strong interactors (i or I, respectively) with weak or strong metabolized drugs (m or M, respectively): I/M, I/m, M/I, i/m, or no interaction. Lastly, the mean value of R AUC provided by DDI-Predictor was categorized as R AUC ≤ 0. 5, R AUC 0. 5-2 or R AUC > 2. R AUC ≤ 0. 5 and R AUC > 2 correspond to at-risk DDIs. For each tool, interactions that did not fit into any of the defined categories were noted as "not recorded".
Opioid analgesics and maintenance treatments, benzodiazepines and z-drugs, and other sedatives and stimulants are increasingly being abused to induce psychoactive effects or alter the effects of other drugs, eventually leading to dependence. Awareness of prescription drug abuse has been increasing in the last two decades, and organizations such as the International Narcotics Control Board has predicted that, worldwide, prescription drug abuse may exceed the use of illicit drugs. Assessment of prescription drug abuse tackles an issue that is hidden by nature, which therefore requires a specific monitoring. The current best practice is to use multiple detection systems to assess prescription drug abuse by various populations in a timely, sensitive, and specific manner. In the early 2000's, we designed a method to detect and quantify doctor shopping for prescription drugs from the French National Health Data System, which is one of the world's largest claims database, and a first-class data source for pharmacoepidemiological studies.
HOW TO QUANTIFY DOCTOR SHOPPING? Doctor shopping is difficult to monitor because the patient often attempts to hide the abuse and the prescribers may not even realize that they have been deceived. These observations underline the limitations of interviewing the prescribers or patients, and therefore, highlight the added value of claims databases to quantify doctor shopping objectively. Several teams from different countries have developed methods to detect doctor shopping in claims databases  (13) (14) (15) (16) (17) (18) (19). The methods face two main challenges: a proper design of the method to accurately detect drug-abusing patients and the use of a data source that is representative of the population of interest.
g. , inconvenient hours or locations, long waiting times, or personal characteristics of the prescriber), illness factors (e. g. , persistence of symptoms, lack of understanding, or lack of confidence in diagnosis or treatment), or psychological factors (e. g. , anxiety leading to dose stockpiling)  (36, 37). Conversely, overlapping prescription is at the core of the safety concern, because it is the reason for the lack of medical management. Interestingly, a study compared the diagnostic odds ratios for opioid overdose of nine definitions of pharmacy shopping, using a multistate Medicaid claims database in the USA  (38). The overdose rate was higher in patients with overlapping prescriptions than in patients with only pharmacy shopping. In addition, another study quantified episodes of multiple prescriber for benzodiazepines using a twoyear cohort in Japan.
Second Challenge: A Representative Data Source To enable an accurate quantification of doctor shopping, the database must be representative of the population of interest. In addition, the database must identify each health professional and health care consumer by a consistent pseudonym over time and across geography. Given that claims databases were initially designed for medicoadministrative purposes, it is far from trivial in practice. For example, in the USA, health insurance plans only cover residents by states or focus on a specific subset of the population (e. g. , Medicaid covers low-income populations, while private insurances are employment-based). Notably, the use of a non-representative population may bias the results, because socioeconomic status is associated with abuse (47-49).
In addition, some regulation [e. g. , the 42 CFR part 2 in the USA, which aims to ensure confidentiality of records from federally funded drug and alcohol treatment centers  (50) ] may further complicate the use of claims databases. In this regard, the French National Health Data System is a first-class data source for pharmacoepidemiological studies, as one of the world's largest claims database, whose representativeness is almost perfect (51-53). The National  Health Data System prospectively merges pseudonymized records of claims from all the French health insurance plans, the national hospital-discharge database, and the national death registry  (54). Because the coverage by a health insurance plan is mandatory in France, the French National Health Data System covers almost 100% of the 67 million inhabitants, from birth to death, independently of the socioeconomic status and region of residence.
ADDED VALUE OF DOCTOR SHOPPING MONITORING TO IMPROVE THE EARLY DETECTION OF PRESCRIPTION DRUG ABUSE WITHIN A MULTIDIMENSIONAL MONITORING In order to face the challenges of monitoring prescription drug abuse, several authors and health authorities advocate for a multidimensional proactive post-marketing monitoring  (10) (11) (12). Such a multidimensional monitoring is already operational in France through the French Addictovigilance Network  (85) (86) (87) (88) (89) (90). In addition to a spontaneous notification by health professionals and pharmacoepidemiological studies from claims databases  (91, 92) , multiple ad hoc studies have been conducted nationwide, such as: the OSIAP program, to detect forged prescription  (93, 94) ; the OPPIDUM program, to detect psychoactive drug use in drug-abusing patients  (95, 96) ; the DRAMES program, to detect deaths related to psychoactive drugs; the DTA program, to detect deaths related to analgesic prescription drugs; or the chemical submission program, to detect psychoactive drugs administered without the victim's knowledge  (97).
DISCUSSION The paper aims to review how doctor shopping monitoring can improve the early detection of prescription drug abuse within a multidimensional monitoring. The paper provides an in-depth overview of two decades of development and validation of the method as a complementary component of the multidimensional monitoring conducted by the French Addictovigilance Network. In this context, doctor shopping monitoring has demonstrated its added value to improve the early detection of prescription drug abuse. Notably, the monitoring must also include a strong pharmacological expertise, which is essential to both analyze signals and interpret pharmacoepidemiological data. While the method has been developed and validated in France, the rationale is transposable in other health systems with available claims databases.
In practice, given the increasing availability of claims databases in several countries, the main issue is to integrate doctor shopping monitoring within a multidimensional monitoring. In addition, the method must undergo an in-depth customized validation process, accounting for the specificities of the targeted health system (e. g. , availability of prescription drugs and illicit alternatives, cost of prescription drugs and visits, prescription and control methods, and risks involved for fraud). Such pharmacoepidemiological monitoring is intended to develop in the big-data era. Interestingly, it is nowadays technically possible to implement a real-time doctor shopping monitoring, assuming that a quick access to data is available, which is currently the bottleneck. Finally, as a public health mission, monitoring prescription drug abuse must rely on free from conflict-of-interest organizations to prevent private interest from interfering, as it was the case in the opioid crisis  (107, 108).
When a multidisciplinary patient safety team was assembled to detect and analyze ambulatory medical errors by using a reporter-anonymous non-punitive process, a voluntary, team approach was effective in improving VRE reporting  [4]. Several other studies have reported on specific VREs including extra immunization  [5, 6] , improper route  [7, 8] , and wrong drug administration  [9]. Reconstitution-related VREs have been explored in several studies in Europe. A Belgian observational study and a European database study found 5-times and 2. 5-times fewer mishandling or preparation errors when using fully-liquid ready-to-use hexavalent vaccines compared to reconstituted hexavalent vaccines  [10, 11]. In a physician's survey in France, 28% reported occasionally omitting to reconstitute and 60% reported having not fully reconstituted vaccines  [12].
Simplified vaccine preparation steps may save time and reduce potential VREs. The literature suggests that vaccines could be administered faster using a fully liquid vaccine compared to a vaccine requiring reconstitution  [5]. One observational study in Belgium comparing hexavalent vaccines in these two formats found that preparation of a fully liquid vaccine can be completed in half the time necessary to prepare a non-fully liquid vaccine  [10] , and that five times fewer mishandling errors occurred when using a fully liquid RTU hexavalent vaccine instead of a non-fully liquid reconstituted hexavalent vaccine  [10]. These errors included missed reconstitution, incomplete aspiration, loss of sterility due to touching of the rubber vial cap, and reuse of the same needle or needle twisting. Similarly, a European adverse events database reported 2.
Static Contact Angle (CA) Static contact angle (CA) measurements were performed by the goniometric method at RT with a Drop Shape Analysis System, model DSA100, equipped with DSA3 ® software (Kruss GmbH, Hamburg, Germany) using either deionized water or diiodomethane as standard solutions. A medium measuring 1 µL was dropped under the control on the sample surface, constantly preserving both the droplet volume and dropping distance. The droplet image was acquired under a 2 • angle with respect to the surface of the examined sample. Solid surface free energy (SFE) calculations were performed based upon the concept of polar and dispersion components (Owens-Wendt approximation)  [55] [56] [57]. Duplicate experiments were carried out in each case and results are presented as mean value ± standard deviation (SD).
For the cellular cycle assay, MG63 cells were cultivated into a 1640 Roswell Park Memorial Institute (RPMI) medium (GIBCO TM by Thermo Fisher Scientific Inc, NY, USA) supplemented with 10% heat-inactivated bovine serum and penicillin/streptomycin and incubated at 37 • C in 5% CO 2 for 24 h. Monolayers were then harvested, washed with phosphate buffered saline (PBS), fixed in 70% cold ethanol, and incubated overnight at -20 • C. Each sample was washed in PBS, treated with 100 µg/mL RNase A for 15 min, and stained with 10 µg/mL PI by incubation at 3 • C for 1 h. Events acquisition was carried out with an Epics Beckman Coulter ® flow cytometer (Beckman Coulter Inc. , Nyon, Switzerland). The data obtained were analyzed using the FlowJo TM software (FlowJo LLC, Ashland, OR, USA) and expressed as fractions of cells under different cell cycle phases.
Mass Loss The mass, M 0 , of as-deposited composite film was measured for 16 samples. An average weight of deposited films of 71 ± 7 µg was inferred, with a SD of ±10%, which is due to both the inherent mass variation between MAPLE samples deposited under identical conditions and the accuracy of the weighting measurements. The mass loss from film, ∆m(t), was estimated with Equation (1). Measurements were conducted for t (immersion duration in SBF) of 2, 4, 6, 8, 12, 24, 48, and 72 h, respectively, and the results were represented in Figure  9. A continuous increasing trend of ∆m(t) is noticed from Figure  9 , with a saturation tendency towards longer immersion durations. The experimental data were fitted with a polynomial law of the lowest order, in this case quadratic: This law can serve to predict the time behavior of such composite mixture deposits in SBF, and by extension in BF (actual body fluids), i.
Eleven clinical trials are currently referenced worldwide (https://clinicaltrials. gov/, accessed  January 4, 2021). They include the study of using inorganic nanoparticles, such as silver nanoparticles (antibacterial treatments), quantum dots coated with a somatostatin analog (breast cancer imaging and treatment), nanocrystals of hydroxyapatite (chronic periodontitis), nanocrystals of curcumin (oral aphthous ulcer treatment), nanoparticles encapsulating capsaicin (painful diabetic neuropathy treatment), and nanoparticles of chitosan encapsulating chlorhexidine (pulp necrosis treatment). The combination of nanotechnologies with hydrogels is increasingly being considered for medical applications. Over the past decade, promising preclinical results have been reported concerning the use of liposomes, micelles, liquid and solid lipid nanocapsules, polymer nanoparticles, dendrimers, and fullerenes.
Confocal microscopy DiO-loaded LNC-based hydrogels and DiO-loaded LNC suspensions were deposited on glass slides (24 × 36 mm) and observed by confocal microscopy on a Leica TSC SP8 AOBS (Leica Microsystems, Germany) using LAS X software. Images were captured with a HC PL APO CS2 63×/NA 1. 40 oil objective and a 3× zoom. The excitation of DiO was performed with the 488 nm line of an argon ion laser (40 mW) and the fluorescence emission collected using a gateable GaAsP hybrid detector between 492 and 600 nm.
Acknowledgements This work was carried out within the  research program  of  NICHE  (EuroNanoMed2 -4th call) and GLIOGEL (EuroNanoMed3 -7th call). The authors also thank the  « Fondation ARC pour le Recherche sur le Cancer » and the « Ligue Contre le Cancer  ( Comité du Maine-et-Loire ) » for their financial support. We thank the  Pathology Department of the University Hospital of Angers  for technical assistance in the hematoxylin-phloxin-saffron staining of the tissue sections. We are also grateful to the  SCAHU at Angers University  for the use of their animal facilities.
In order to broaden the range of our biological tests on these derivatives taking into account the antiviral effect of oleanolic and maslinic acids as well as of isoxazoles and triazoles, molecular docking studies of OA, MA and most previously synthesized derivatives has been carried out targeting SARS-CoV-2 main protease which allowed one to select seventeen compounds to be evaluated for their antiviral activity against hCoV-19/Egypt/NRC-03/2020 (Accession Number on GSAID: EPI_ISL_430820) "NRC-03-nhCoV" virus  [40]. Molecular docking is an important method of computational studies which are very promising tools in the new process of drug discovery and development as well  [39]. Concerning the aforementioned crucial rule of main protease (M pro ) for SARS-CoV-2 maturation, and in continuation to our previous work targeting SARS-CoV-2  [2, 3, [41] [42] [43] [44] , we decided to target SARS-CoV-2 M pro using molecular docking studies and we further validated the antiviral activity of the 17 compounds representing OA, MA and their derivatives.
Preparation of the Selected Tested Compounds Each member of the previously mentioned selected compounds (1-17) was sketched using the ChemDraw program, transferred to MOE, and then subjected to the preparation process for the docking step as described earlier  [52]. Then, all the prepared compounds (1-17) were imported in one database together with the co-crystallized native inhibitor (N3, 18) and saved as an MDB file to be used during the docking procedure.
The fixed monolayers were then dried and stained with 50 µL of 0. 1% crystal violet for 20 min on a bench rocker at RT. The monolayers were then washed, dried and the crystal violet dye in each well was then dissolved with 200 µL of methanol for 20 min on a bench rocker at RT. The absorbance of crystal violet solutions was measured at λmax 570 nm as a reference wavelength using a multi-well plate reader. Cytotoxic concentration 50% (CC 50 ) values were calculated using nonlinear regression analysis of GraphPad Prism software (version 5. 01) by plotting log inhibitor versus normalized response (variable slope).
45-μm pore size nitrocellulose membrane (Protran). The membranes were boiled for 10 min in ultrapure water H 2 O directly after transfer and blocked with 5% skimmed milk powder in 0. 3% PBS-Tween20. The primary antibodies were diluted in PBS containing BSA and 0. 02% NaN 3. The membranes were incubated with the primary antibodies overnight at 4 °C. The primary antibodies used were anti-HSF1 (ADI-SPA-901, Enzo Life Sciences), anti-HSF2 (clone 3E2, MAB88079, Sigma-Aldrich or HPA031455, Sigma-Aldrich), anti-HSP70 (ADI-SPA-810, Enzo Life Sciences), anti-FLAG (clone M2, F3165 M2ab, Sigma-Aldrich), anti-Myc (clone 9E10, M5546, Sigma-Aldrich), and anti-β-tubulin (clone AA2, T8328, Sigma-Aldrich). Secondary antibodies conjugated to horseradish peroxidase were purchased from Promega, GE Healthcare, or Abcam.
Acknowledgements We thank the laboratories of  Keith Joung  ( Massachusetts General Hospital ) and  George Church  ( Harvard University ) for reagents for generating the CRISPR cell lines. We thank  Jean-Paul Concordet  ( MNHN, Paris ) for guidance with the CRISPR cell lines. Ingrid Karppi  is acknowledged for her assistance with GBA experiments while in Roos-Mattjus lab. We thank  Lea Sistonen ,  Jenny Joutsen , and the members of the  Sistonen laboratory  for their valuable comments and critical review of the manuscript; members of the  Mattjus laboratory  for technical support; and  J Peter Slotte  for all the support as the department head when this study was initiated.
Funding Open access funding provided by  Abo Akademi University (ABO). This study has been funded by  Åbo Akademi University Research Foundation (PRM) ,  Magnus Ehrnrooth Foundation (PRM) ,  Swedish Cultural Foundation in Finland (PRM) , and  Medical Research Foundation Liv och Hälsa (PRM). VM was funded by the  CNRS (Project International de Scientifique   PICS 2013-2015 ) for her collaboration with PRM and by the  Short Researcher Mobility France Embassy/MESRI-Finnish Society of Sciences and Letters ; this study has been funded by the  Agence Nationale de la Recherche  (« HSF-EPISAME », SAMENTA  ANR-13-SAMA-0008-01 ; VM). This study contributes to the  Université de Paris IdEx  # ANR-18-IDEX-0001  funded by the  French Government  through its " Investments for the Future" program  and the  Fédération Hospitalo-Universitaire  " Early Identification of Individual Trajectories in NeuroDevelopmental Disorders " ( FHU I2D2 ).