dataesr/scientific-paragraphs-categorization huggingface dataset:
X-Ray Diffraction (XRD) Analysis The crystallinity of freeze-dried NLCs was examined by performing XRD analysis using a Bruker D8 Advance (Bruker GmbH, Karlsruhe, Germany) diffractometer in thetatheta reflection mode with copper anode. The frozen aqueous NLC dispersions were freezedried under deep vacuum at -55 • C using a ScanVac CoolSafe freeze dryer (LaboGene ApS, Lynge, Denmark). Diffractograms were obtained at room temperature for all samples. For XRD analysis, samples were sandwiched between two Mylar films and placed in sample holder. Spectra were collected in transmission mode using CuK α radiations at a scan rate of 0. 02 • /sec from 2 to 47 • 2θ values by rotating the sample cell at 15 rpm. Data collection and interpretations were performed using DiffracPlus and the EVA V. 16 program, respectively.
The higher standard deviation for the mean particle size of F30 and F50 OLZ-loaded formulations can be associated with the higher liquid lipid content in these systems  [40]. In comparison, the recorded PDI and particle size values for drug loaded NLCs were similar to those for blank NLCs. This can be due to the solubility of OLZ in the lipid phase, where drugs may be accommodated within the disordered crystalline lipid structure, resulting in no change into the NLC's original morphology  [39] [40] [41]. Table  4  presents the surface charge data recorded for OLZloaded NLCs prepared with varying Mig content at a constant HSH and US time of 15 and 5 min, respectively. Similar to particle size and PDI (Table  3 ), there was a minimal effect of increasing Mig amount on the ζ-potential of drug-loaded NLCs.
In comparison, the recorded PDI and particle size values for drug loaded NLCs were similar to those for blank NLCs. This can be due to the solubility of OLZ in the lipid phase, where drugs may be accommodated within the disordered crystalline lipid structure, resulting in no change into the NLC's original morphology  [39] [40] [41]. Table  4  presents the surface charge data recorded for OLZ-loaded NLCs prepared with varying Mig content at a constant HSH and US time of 15 and 5 min, respectively. Similar to particle size and PDI (Table  3 ), there was a minimal effect of increasing Mig amount on the ζ-potential of drug-loaded NLCs. The high negative ζ-potential values recorded for the drug-loaded NLCs indicate that these colloidal suspensions are stable and less likely to aggregate  [36]. Interestingly, the ζ-potential data recorded for drug-loaded preparations were noticeably higher than those for blank NLCs, which could be attributed to the presence of OLZ on the surface of the particles.
BF-STEM Analysis and Encapsulation Efficiency The surface morphology of drug-loaded NLCs was investigated by BF-STEM and is presented in Figure  6. The BF-STEM micrograph (Figure  6 ) showed that the prepared drug-loaded NLCs were spherical with a smooth surface. The particle size indicated by BF-STEM imaging was also similar to the data recorded by DLS for the formulated drug-loaded NLCs. The encapsulation efficiency (EE) of OLZ increased with the increase in Mig content in the formulation. The EE ranged from for all formulations with F5 with the lowest and F50 with the highest. The EE obtained was 33, 36, 41 and 60% for F5, F15, F30 and F50, respectively. The higher encapsulation efficiency with increased Mig content in the formulation could be due to the lipophilic nature of OLZ. Although compritol itself is a long chain fatty acid that can facilitate the entrapment of OLZ in NLC, clearly an increase in Mig has substantial impact on resulting encapsulation in these formulations  [44].
ATR-FTIR Analysis of OLZ-Loaded NLCs The structural features of drug-loaded NLCs were studied by performing ATR-FTIR analysis, and the ATR-FTIR spectra for C888, Mig and OLZ were also collected to allow for a comparative analysis. As a result of the similarity in their structural composition, both the solid and liquid lipid do not show many differences in their ATR-FTIR spectra. For Mig and C888 (Figure  7a ), major and common peaks were observed at 2915 and 2849 cm -1 due to symmetric and asymmetric aliphatic CH 2 vibrations, and at 1742 cm -1 as a result of the presence of carboxylic groups  [45, 46]. The BF-STEM micrograph (Figure  6 ) showed that the prepared drug-loaded NLCs were spherical with a smooth surface. The particle size indicated by BF-STEM imaging was also similar to the data recorded by DLS for the formulated drug-loaded NLCs.
Therefore, the kinetic data suggests that the NLCs are not only capable of increasing the drug dissolution rate but their liquid lipid content can also influence the release of incorporated drug. The drug release in this work was studied only in pH 7. 4 PBS, which provides a general overview of the release behavior along with the impact of the ratio between solid and liquid lipids as well as the drug content in the formulation. However, it will be appropriate to conduct further studies in simulated gastric and intestinal fluids to understand the influence of variable gastrointestinal conditions on the release of olanzapine from NLCs. Kinetic evaluation of drug release profiles from different NLCs was also performed to understand the mechanisms governing the drug release. The release data were fitted into zero order, first order, Higuchi and Korsmeyer-Peppas models.
However, it will be appropriate to conduct further studies in simulated gastric and intestinal fluids to understand the influence of variable gastrointestinal conditions on the release of olanzapine from NLCs. Kinetic evaluation of drug release profiles from different NLCs was also performed to understand the mechanisms governing the drug release. The release data were fitted into zero order, first order, Higuchi and Korsmeyer-Peppas models. The calculated correlation coefficients (R 2 ) and release exponent (n) values are presented in Table  5. The best fit for drug release data from NLCs was obtained for Korsmeyer-Peppas kinetic model with R 2 values ranging from 0. 9624 to 0. 9861. The value of the diffusional release exponent (n) of the Korsmeyer-Peppas equation was calculated from Equation (3): F t = k × t n (3) Where, F t is the drug fraction released at time t, k the release constant and, n the diffusional exponent relates to the mechanism of drug release.
A steady and continuous release of drug from the OLZ-loaded lipid systems was recorded in PBS at pH 7. 4 in 24 h, with a maximum drug release of 89% achieved in formulations prepared with 5% liquid lipid in the formulation that was approximately nine-fold higher than the drug alone. The in-vitro release in this study was performed only in pH 7. 4 PBS that provided a general overview of the drug release from formulated NLCs. In future studies, it would be useful to determine the drug release profiles in simulated gastric and intestinal environments to establish the suitability of these formulations for oral administration. The release data obtained from this work indicated that liquid lipid content in NLCs can influence the release of loaded drugs and it is important to study the processing parameters carefully when using US in combination with HSH whilst developing a protocol for NLP preparation.
Changing spectrum of suspected drugs of epidermal necrolysis: A World Health Organization pharmacovigilance database analysis from 1997-2020 To the Editor: Epidermal necrolysis (EN), including StevenseJohnson syndrome and toxic EN, is a rare life-threatening disease characterized by diffuse skin and mucosal detachment. 1  Between 70% and 90% of cases of EN are caused by drugs. Prompt identification and discontinuation of the suspected drug(s) are vital. 1, 2 To assess the changing spectrum of drugs associated with EN, we performed a multicentre retrospective study using VigiBase, the World Health Organization global database of individual case safety reports  3  (Appendix I available via Mendeley at https://data. mendeley. com/datasets/ nzgxjdtzx4/1). This study included all cases of EN reported in VigiBase.
OUD Characteristics Data concerning the main substance used was collected using closed, multiple-choice questions on the type of opioid used (heroin, non-medical use of codeine, morphine, buprenorphine, and methadone), age at the initiation of opioid use, age when the opioid dependence appeared, age at the first attempt to quit, the main route of administration (nasal, intravenous, inhaled, oral), strategies used previously to attempt to quit opioid use, and type of damage (negative consequences) related to opioid dependence (financial, socio-affective (familial, relational), psychiatric, professional, legal, physical problems) self-reported by the patients. OMT characteristics were also collected (type of medication, duration).
Strengths and Weaknesses This study collected a large multicenter dataset concerning patients with an OUD and on OMT in daily clinical practice. This provided heterogeneity across the population. Missing data was marginal. Our study does however present limitations. Firstly, its design was cross-sectional, and variables were self-reported by patients. Patients were recruited in centers specialized in addictive disorders or in primary care settings trained for OUD management, but data regarding their addiction treatment histories was not collected. BHD-NX was not included in the analysis, as no patient included had this treatment, but in France, despite its marketing in 2012, BHD-NX still accounts for only 5% of BHD/BHD-NX prescriptions  [16]. Secondly, we developed an ad hoc structured interview to explore OUD characteristics (especially the damage incurred).
The production probability score of the edge is determined by using site of metabolism (SOM) predictors (see methods for details) and the atom number of reaction. A specific node is identified in the graph, named the original compound. It is defined as the source of the map of metabolism, and the compound is described by its SMILES canonical formula available in PubChem database  [39]. We then introduce a rank label for each edge, which describes the position of the reaction in the graph with respect to the original compound: first rank reactions correspond to edges having the original compound as input, second rank reactions corresponds to edges whose input is the output of a first rank reaction, etc. Pipeline for building a map of metabolism Maps of metabolism were built using a combination of several tools, which are precisely described in the Methods section.
The pipeline is shown in Fig. 3  and starts with the selection of an original compound described by its SMILES canonical formula available in PubChem database. In this paper, the method was applied to 31 original compounds: caffeine (for the sake of validation of the method), and 30 HAA, see Results below. For each original compound, the SyGMa python package  [22]  is applied to compute nodes (e. g, metabolites of the original compound) and edges (e. g. , transformations between metabolites) of the associated map of metabolism. In our studies, SyGMa was iterated twice, in order to predict first-rank and second-rank reactions with respect to Fig. 3  Pipeline for building an enriched map of metabolism. Scheme representation of the pipeline to obtain labels to enrich a metabolism map. Labels are depicted in green (metabolites) and blue (reactions) squares.
They are computed using the various tools and methods (grey squares) which require as input the information provided by other labels. SMIRKS labels dictionary is detailed in Additional file 2 the original compound, e. g, all possible metabolites of the original compound with at most two transformations. The SMILES label (and its associated 2D structure) of each node is generated by the RDKit package implemented in SyGMa. To define the DNA reactivity label of each node, we consider that a node is reactive with DNA if at least one of the atoms of the metabolite has a score of reactivity computed by XenoSite Reactivity  [10]  greater than 0. 85. For each edge, a manual curation procedure is performed to determine the atom number of reaction. This label represents the number of the atom, according to IUPAC numbering  [37] , of the input metabolite of the edge, on which the reaction occurs to produce the output metabolite of the edge.
We also noticed that the maps associated with pairs of HAA such as 7,8-DiMeIQx and 7,9-DiMeIgQx, MeIQx and 7,MeIgQx or IQ and IQ  [4,5-b]  are associated with maps with similar characteristics: they have the same number of metabolites, reactions and metabolites reactive to DNA. The closeness of the chemical structure of isomers could explain such a similarity between maps: as planar isomers are composed of the same chemical substructures, the transformation rules contained in the SyGMa database have a high probability to occur equivalently on the metabolites of both HAA. For each HAA, we estimated the number of metabolites reactive to DNA by assuming that each metabolite with a XenoSite Reactivity score greater than 0. 85 is reactive to DNA, following the criteria introduced in  [36]. The four HAA, 4-CH 2 OH-8-MeIQx, 7,8-DiMeIQx, 7,9-DiMeIgQx and 4,7,8-TriMeIQx have both the largest map and the greatest amount of metabolites reactive to DNA (91, 80, 81 and 81).
Tool for the prediction of edges and nodes of maps of metabolism: SyGMa The SyGMa python package (Systematic Generation of potential Metabolites)  [22]  is a rule-based method to predict metabolite (e. g. derivative chemical compounds) from an input chemical compound. In the paper, the input compound was either caffeine or HAA. The method relies on an internal set of metabolic reactions (biotransformation rules) in SMIRKS format which can be applied to the input compound. If the input chemical structure of a SMIRKS reaction is detected in the compound, the reaction is applied and the resulting structure obtained is a predicted metabolite. The first parameter required by SyGMa to make predictions is the set of SMIRKS reactions to use. In this paper, we used the two sets of SMIRKS reactions, named "phase I" and "phase II", obtained by data mining the Metabolite Database  [22]  and corresponding to reactions involved in phase I and II metabolism of xenobiotics.
Tools for the prediction of sites of metabolisms (SOM) We used tools for site of metabolism (SOM) prediction tools to annotate reactions that could be catalysed by phase I or phase II enzymes of xenobiotics metabolism. Each method used for predicting SOMs estimates the capability of an enzyme to react with an atom according to a reference database of reactions and their metabolites. Using these models, each method can evaluate each atom of a compound to see if it can be involved in a reaction similar to those described in the reaction database. This analysis results in a score for each atom of a compound that describes the probability to be transformed by each reaction. Therefore, the methods differ according to the parameters describing the atom configuration and the enzymes involved in the reactions (different enzyme family and/or isoforms).
From preclinical to clinical investigations Many preclinical studies demonstrated the generation of a potent immune response in several animal models  [19] [20] [21] [22] [23] [24]  and some were pursued by clinical trials, performed to assess prophylactic sublingual vaccines efficiency in humans (Table  1 ). In a phase I trial (NCT01488188), nasal vaccine Flumist® was administered under the tongue to overcome difficulties of nasal administration in infant and potential side effects in elderly patients such as rhinitis and sneezing episodes. Sublingual Flumist® administration led to comparable and promising results in terms of immunogenicity and safety when compared to nasal administration. However, to date, no sublingual vaccine is commercially available, except Uro-mune® (MV140), which is a therapeutic vaccine for recurrent urinary tract infections (RUTI).
Safety and efficacy were demonstrated in several prospective studies  [25, 26] , as well as phase 2 and phase 3 clinical trials (NCT04096820, NCT02543827) through daily administration of 2 sprays under the tongue for at least 3 months. To date, a potential answer proposed for vaccines against challenging infectious agents or problematic unstable compositions would be subunit antigens. However, these formulations, based on peptides or proteins (such as surface proteins) delivery, need to be carried along with appropriate adjuvants to improve their immunogenicity. In addition, as the buccal mucosa is a pro-tolerogenic tissue which evolved to avoid undesired immune responses against innocuous molecules, sublingual vaccine design is particularly challenging and requires clinical trials to assess adjuvants safety and efficacy  [27].
The most studied mucosal adjuvants for sublingual administration are ADP-ribosylating enterotoxins including heat-labile enterotoxin of Escherichia coli (LT), cholera toxin (CT), and their associated mutants or subunits (reviewed in  [28, 29] ). The safety of sublingual administration of B subunit of CT (CTB), was evaluated in a phase I clinical trial (NCT00820144) (Table  1 ) and has been successfully used as an antigen carrier (or mucosal vector) in preclinical studies  [20]. Another promising adjuvant is double LT mutant (dmLT or R192G/L211A) (reviewed in  [28] ). Indeed, the moderate immunogenicity of dmLT (even at doses up to 50 μg) impedes its use as an enterotoxigenic Escherichia coli (ETEC) sublingual vaccine  [30]  but confirms its use as a potent mucosal adjuvant for subunit vaccine, such as SHIV gp120-based vaccines in macaques  [31].
However, a randomized one-session clinical trial (NCT03629041) was recently performed by applying a 700 μm long stainless-steel solid microneedle patch on various regions of the oral cavity (lip, buccal/cheek, tongue, hard palate and gingiva) of 30 volunteers. This study concluded on the safety and significantly reduced pain as compared to a hypodermic needle  [68] , a favourable result for vaccine administration by microneedle arrays.
coli, ETEC) Toxin LT (dmLT) A Double-Blind Placebo-Control Phase I, 2018-2020 135 Gastroenteritis Recombinant Double - NCT03548064 Dose Escalating Study to Evaluate double-blind, (Enterotoxigenic Mutant Heat-Labile the Safety and Immunogenicity of randomized E. Coli, ETEC) Toxin LT (dmLT) dmLT by Oral, Sublingual and Intradermal Vaccination in Adults Residing in an Endemic Area Mucosal adjuvants Effect of interferon-α Citizen by Phase III, 2005-2008 140 Flu 2005 flu vaccine (i. IFNα 2b (Intron ATM) NCT00647465 Sub-Lingual Way on the Humoral double-blind, m. injection of Immunizing Answer (GP-INFA) randomized InfluvacTM) Cholera Toxin B Subunit (CTB) Phase I, open, 2006-2009 40 - - Recombinant Cholera NCT00820144 Administered by Mucosal Way in randomized Toxin subunit B Healthy Adult Volunteer (CTB).
= (2) Using the Technobis Crystal16 instrument, temperature cycles were applied to multiple samples of identical compositions. The initial suspension was heated beyond the saturation temperature to ensure complete dissolution; then, the solution was cooled rapidly to obtain the desired supersaturation ratio, S. The time between reaching the supersaturation ratio, S, and the first appearance of crystals, indicated by a drop in transmissivity, was taken as the induction time, tind. These cycles continued until 80 data points were collected at the same supersaturation ratio  [22] , and the experimental induction time probability distribution was plotted according to Equation  (3) , where M + (t) is the number of induction time measurements in which crystals were detected at time t, and M is the total number of induction time measurements at an equal supersaturation ratio  [23, 24].
( ) = ( ) (3) The induction time distribution was then fitted to the probability distribution function in Equation (4)  [23] , where J is the nucleation rate, V is the experimental volume, t is the true induction time and tg is the time difference between nucleation occurring and the crystals being observed. J and tg are fitted parameters, obtained from the experimental data using Origin 3. 0. ( ) = 1 -exp (- - ) (4) Small-scale batch crystallization experiments were carried out using the Technobis Crystalline platform. Approximately 1 g paracetamol was weighed into a vial containing a magnetic stirrer bar, followed by an amount of impurity and 4 mL of 2-propanol. The relative impurity spike (I) of this crystallization feed was calculated using Equation  (5)  based on the molar ratio of impurity (mi) and API (mAPI) in the feed suspension (f).
Product Analysis HPLC was used to measure component concentrations in the solid and isolated liquid phases after crystallization and reslurrying. Calibration curves with a detector wavelength 243 nm were constructed to allow accurate concentration determination. HPLC analyses were carried out using an Agilent 1100 instrument fitted with an Agilent Poroshell 120, EC-C18, 4. 6 × 74 mm 2. 7 um column. A mobile phase of 20 v% MeOH 80 v% water was used with an isocratic elution of 1 mL min -1 at a column temperature of 40 °C. Samples were prepared by dissolution in 5 v% MeOH 95 v% water solvent mixture, and then syringe filtered to remove particulates. Analysis was stopped after 12 min, and the resulting chromatograms were interpreted using the Agilent ChemStation software. The purity of a solid Xp was calculated from the number of moles of API present and the number of moles of impurity present (Equation (  7 )), as indicated by the HPLC data.
The concentrations of impurity in the solid phase after crystallization ( , ) and after reslurrying ( , ) were calculated in a similar fashion (see Equation  (8)  for Xi,p). = + • 100 (7) , = + • 100  (8)  The polymorphic form of the API crystallized was analyzed using a Bruker AXS D8 Advance II diffractometer using a Debye-Scherrer transmission of Cu Kα1 radiation with a wavelength of 1. 540596 Å. Samples were prepared on an automated x-y 28-well plate with 7. 5 μm Kapton film. Patterns were collected on a range of 4-35 2θ, and the paracetamol phase was confirmed using Pawley refinement against reference patterns located in the Cambridge Crystallographic Data Centre (CCDC) to identify the polymorph present. Thermal analysis was carried out using a Netzsch DSC 214 Polyma. A single heatingcooling cycle was used to avoid polymorphic transformations  [25].
Samples were sealed in aluminum pans with a pierced lid at 20 °C and then heated at rate of 10 °C min -1 to 190 °C. After a 10 min isothermal hold at the maximum temperate, the sample was then returned to room temperature at a rate of 10 °C min -1. Melting temperatures and signal area were determined using Proteus Analysis software. Using the literature-reported values for the solubility of paracetamol, metacetamol and acetanilide in acetone  [26] , volumetric stock solutions of each solute with the same molar concentration were prepared. These solutions were then combined in different binary ratios to give solutions of different impurity mole fractions. These solutions were evaporated at room temperature to give crystalline material suitable for Differential Scanning Calorimetry (DSC) analysis.
The melting temperature of the component and eutectic signals were plotted as a function of the impurity mole fractions to give the experimental binary phase diagram. Where the DSC graphs contained a eutectic peak, the area of this signal was plotted as a function of the impurity mole fraction in the solid phase. A linear fit of these data points gave two linear relationships which intersect at a maximum energy value, with an impurity mole fraction value corresponding to the eutectic composition  [27]. The miscibility limit of the solid-solid composition was determined by the linear extrapolation to the intersection of the impurity mole fraction axis. Small amounts of crystals were transferred to microscope slides and viewed with an appropriate magnification to identify any agglomeration. Images were captured using a Leica DM6000 FS microscope.