dataesr/scientific-paragraphs-categorization huggingface dataset:
Crystal Structure Determination Data collection was performed on a Bruker D8 Venture four-circle diffractometer from Bruker AXS GmbH. CPAD detector used: Photon II from Bruker AXS GmbH; X-ray sources: Microfocus source IµS and microfocus source IµS Mo and Cu, respectively, from Incoatec GmbH with mirror opticsHELIOS and single-hole collimator from Bruker AXS GmbH. Programs used for data collection: APEX4 Suite  [57]  (v2021. 10-0) and integrated programs SAINT (V8. 40A; integration) and SADABS (2018/7; absorption correction) from Bruker AXS GmbH  [57]. The SHELX programs were used for further processing  [58]. The solution of the crystal structures was done with the help of the program SHELXT  [59] , the structure refinement with SHELXL  [60]. The processing and finalization of the crystal structure data was done with program OLEX2 v1.
5  [61]. All non-hydrogen atoms were refined anisotropically. For the hydrogen atoms, the standard values of the SHELXL program were used with U iso (H) = -1. 2 U eq (C) for CH 2 and CH and with U iso (H) = -1. 5 U eq (C) for CH 3. All H atoms were refined freely using independent values for each U iso (H). Crystal data for C 41 H 41 N 3 O 6 , M = 671. 77 g•mol -1 , dark orange crystals, crystal size 0. 781 × 0. 534 × 0. 378 mm 3 , monoclinic, space group P2 1 , a = 11. 6919(4) Å, b = 22. 6612(7) Å, c = 13. 0938(4) Å), α = 90 • , β = 90. 3920 (10)  • , γ = 90; V = 3469. 15(19) Å 3 , Z = 4, D calc = 1. 286 g/cm 3 , T = 100 K, R 1 = 0. 0296, wR 2 = 0. 0749 for 45555 reflections with I > = 2σ (I) and 14594 independent reflections. Largest diff. peak/hole/e Å -3 0. 14/-0. 17. Flack parameter 0.
Molecular Modelling 3. 5. 1. Molecular Docking The crystal structure of SARS-CoV-2 M pro complexed with the reversible inhibitor "X77" (PDB code: 6W63)  [50]  was used to conduct docking experiments on GOLD (version 5. 8)  [51, 52]  as previously described  [53]. All figures were prepared using PyMol  [62].
Molecular Dynamics Simulations MD simulations were done utilizing the PMEMD. cuda code of the AMBER Molecular Dynamics package  [54]  following the same previously described protocol of minimization, heating, density equilibration and production  [53]. Each system was heated for 20 ps followed by density equilibrations for 200 ps and finally the production run was for 80 ns. All simulations were run on an Intel core i7 @2. 60 GHz workstation with Nvidia GeForce GTX 1660 Ti GPU with an average CPU time of 100 ns/day. Analysis of the trajectories were done using CPPTraj  [63] , VMD  [64]  was used to visually examine the trajectories and RMSD Plots were generated using XMgrace  [65]. Approximately 15192 TIP3P water molecules were used to solvate each ligand-M pro complex within a cubic box of dimensions 91.
Free Energy Calculations The MM-GBSA method  [56]  was used to compute the binding free energy using the MMPBSA. py script  [68]  included in AmberTools  [54]. The calculations were conducted for the last 10 ns of the MD trajectory with snapshots sampled at a regular interval of 10 ps ending up with a total of 1000 frames. The MMGBSA estimates the binding free energy (∆G bind ) as, ∆G =∆E MM + ∆G sol -T∆S, where ∆E MM accounts for the bonding, electrostatic and van der Waals interactions as in standard molecular mechanics. The solvation energy, ∆G solv , is the sum of the polar and non-polar contributions of solvation, with the former calculated using a Generalized-Born model while the latter contributions are computed based on the size of the solvent-accessible surface area in the ligand and M pro.
FRENCH STUDY OF COVID-19 VACCINATION AND HAEMORRHAGIC STROKE The aim of the French study was to quantify the relative incidence of a range of cardiovascular events after vaccination with the Pfizer-BioNTech vaccine (BNT 162b2 mRNA) against SARS-CoV-2 infection and COVID-19 disease, using a SCCS design. 4 e focus on one endpoint: haemorrhagic stroke. The data were extracted from the French national health data system SNDS linked to the vaccination database VAC-SI. The SNDS is a system of linked health-related databases covering 99% of the French population (67 million people). 9, 10, 11, 12 Hospital data from the PMSI database within SNDS comprises admission and discharge information, including diagnostic codes based on the 10th revision of the International Classification of Diseases and Related Health Problems (ICD10).
A 2015 study showed that ICD10 codes for haemorrhagic stroke recorded in the PMSI database had a positive predictive value of 77%. 13  The results reported here are based on a preliminary data set of 2894 cases of haemorrhagic stroke, of whom 894 received at least one vaccine dose. These data were used for investigative purposes; the published study, which used updated data, included 3514 cases aged 75 years and older. 4 he data set used for the present study included all individuals with a first haemorrhagic stroke occurring within a 96day observation period stretching from 15th December 2020 to 20th March 2021. The post-vaccination risk period of interest included days 1 to 14 after each of the two vaccine doses (day 0 was excluded for reasons that will be set out in section 3. 4). The event is sufficiently rare that the SCCS method may validly be applied.
Event-dependent exposures Of the 894 vaccinated cases, 407 had received both vaccine doses. The median interval between doses was 23 days, with minimum and maximum separations of 16 and 49 days, respectively. The distributions of vaccinations (both doses combined) and haemorrhagic strokes by calendar day since 15 December 2020 are shown in Figure  1. This shows a drop in the number of haemorrhagic strokes over time. This is due to the time required to verify the data and update the central SNDS database; these delays are unrelated to vaccination status or time since vaccination. Histograms of the interval between vaccination and the event, for each dose, are shown in Figure  2. The top panel shows all time intervals from dose 1 to event, for events occurring before dose 2 if given (and for all events in cases who received dose 1 but not dose 2); the bottom panel shows the time intervals between dose 2 and the event, for all events occurring after dose 1.
This also applies to other types of censoring completely at random unconnected with mortality (for example, end of the database record). On the other hand, if it is known which deaths are caused by the event and which deaths are not, then observation periods should end at the planned end of observation for cases who die due to the event, and at earliest of death and planned end of observation for deaths not due to the event. This last approach may also be explored when the duration of short-term mortality due to the event is known or may be estimated (as in Table  1 ). Deaths occurring less than days after the event are then nominally attributed to the event, and deaths occurring or more days after are attributed to other causes.
The performance of this test will be investigated in section 4. 3. In version 1. 4 of the R package SCCS, an estimate of this covariance matrix may be accessed after fitting the SCCS model for event-dependent exposures. 16 The R code for this, together with a more general version of the test, applicable when there are more than two doses (or more than one risk period per dose), are described in the Appendix.
SIMULATION STUDIES In section 4. 1 we describe further investigations of the robustness of the model to failure of the assumption that all deaths are due to the event. In section 4. 2, we investigate the need to include all cases, not just vaccinated cases, when undertaking an analysis with the SCCS model for event-dependent exposures. We also study the relative efficiency of the proposed model compared to the standard SCCS model, when event-dependence of exposures is not an issue. Finally, in section 4. 3 we study the power of the test described in section 3. 3 for detecting a true dose effect. All these investigations were undertaken using simulations. The R code for the simulations is available from the Journal website. All SCCS models fitted in the simulations used a nominal observation period of 100 days, risk periods 0 to 14 days after each vaccine dose (there is no day 0 issue as vaccinations occur as the day begins), and no temporal effects.
In these circumstances, the estimates may be much more severely biased if unvaccinated cases are excluded. The purpose of the following simulations is to demonstrate this point. For simplicity, no deaths due to causes other than the event are involved. The simulation set-up was the same as in section 4. 1 with = 0% (no deaths due to causes other than the event). For each run, we fitted two SCCS models for event-dependent exposures: the first was fitted to the whole simulated dataset of 1000 cases. The second was fitted only to those cases in which the event followed a planned vaccination. Thus, events prior to vaccination were excluded in this second analysis: for all such events, it was assumed that any planned subsequent vaccinations were cancelled; these cases are therefore unvaccinated.
Cellular cytotoxicity determined by a LDH (lactate dehydrogenase) assay The CytoTox 96® Non-Radioactive Cytotoxicity Assay kit was used to evaluate the cell death and the number of live cells following the SH-SY5Y cell treatment by (i) retinoic acid (RA) deprived of fetal bovine serum (FBS), (ii) tunicamycin (TUN), and (iii) nanoparticles. This assay quantitatively measures the amount of lactate dehydrogenase (LDH), a stable cytosolic enzyme that is released upon cell lysis. The released protein LDH in the culture supernatants is measured within 30-min by a coupled enzyme assay (Fig. 3 ), which results in the conversion of a tetrazolium salt (iodonitrotetrazolium violet, INT) into a red formazan product. The generated red color intensity is proportional to the number of lysed cells. The absorbance data are collected using a standard 96-well plate reader at a visible wavelength (490 nm).
The ThT fluorescence intensity was investigated in RA-differentiated human neuroblastoma SH-SY5Y cells, which were cultured in the absence (controls) or in the presence of 1 or 5 μM tunicamycin ER-stress inducer. After exposure to TUN for 5 h, the cells were treated with curcumin, fish oil and BDNF-loaded liquid crystalline nanoparticles (MO/ FO/CU/BDNF) LCNPs for 24 h at 5 μM lipid concentration (Fig. 12B  and C ). The fluorescence images in Fig. 12B  show a low-intensity ThT fluorescence in the absence of TUN. The ThT fluorescence was enhanced in the cells exposed to the ER stress inducer. However, the ThT fluorescence diminished in the TUN-stressed cells, which were treated by multicompound-loaded nanoparticles (MO/FO/CU/BDNF) LCNPs. Quantitative results were obtained after image analysis with the ImageJ software.
Acknowledgements The performed research was funded by the " IDI 2017 " project of the  IDEX Paris-Saclay / ANR-11-IDEX-0003-02  and the projects " Structural Dynamics of Biomolecular Systems" (ELIBIO)  ( CZ. 02. 1. 01/0. 0/0. 0/15_003/0000447 ) and "Advanced research using high-intensity laser produced photons and particles" ( CZ. 02. 1. 01/0. 0/0. 0/16_019/0000789 ) from the  European Regional Development Fund. B. A. obtained also a financial support from the collaborative international project with  JINR, Dubna (3þ3 program , No. 204 , item 27 from  25. 03. 2020 ). Y. D. was supported by grants from the  Wenzhou Institute, University of Chinese Academy of Sciences  (Grant No. WIUCASQD2019005 ) and the  National Natural Science Foundation of China  (Grant No:  31670841 ). We gratefully acknowledge the allocation of beam time at Synchrotron Fig.
Rheological Measurements Rheological properties of the TSH and the SV samples were evaluated with a Haake Mars Rheometer (ThermoFisher Scientific, Waltham, MA, USA), using a conic geometry (1 • ) of 35 mm diameter. The gap size was 52 µm and the volume of the sample analyzed was 200 µL. All analyses were performed using the RheoWin Data Manager software (version 4. 82. 0002, ThermoFisher Scientific, Waltham, MA, USA). The viscosity of synthetic vitreous (SV) samples was measured by applying a shear rate from 0. 01 to 300 s -1 at 37 • C, and results were fitted with the Carreau-Yasuda mathematical model to obtain the absolute viscosity (η 0 ). An amplitude sweep from 10 to 600% was performed at constant parameters of 1 Hz and 37 • C to obtain the storage modulus G' of the gels, as a mean of values on the linear-viscoelastic regime (LVR).
Acknowledgments: ATV-D thanks  CAPES  for the personal scholarship. A. G. -G. and V. C. F. M. also thank PPM FAPEMIG researcher grants and VCFM productivity fellowship from  CNPq, Brazil. The authors thank  J. M. C. Vilela  and  M. S. Andrade  for the AFM analysis. We thank  Chris Jopling  ( IGF Montpellier ) for improving the English of the manuscript.
Design and settings This international multicentre study is part of the Very old Intensive care Patients (VIP) project and has been endorsed by the European Society of Intensive Care Medicine (ESICM, http:// www. vipst udy. org). Furthermore, it was registered on ClinicalTrials. gov (ID: NCT04321265) and planned in adherence to the European Union General Data Privacy Regulation (GDPR) directive, which is implemented in most participating countries. The COVIP (COVID-19 in very old intensive care patients) investigation aims to improve and enhance the knowledge about relevant factors for predicting mortality in older COVID-19 patients to help detect these patients early on and prevent a worse outcome. National coordinators were responsible for ICU recruitment, coordination of national and local ethical permissions and supervision of patient recruitment, as in the previous VIP studies  [14, 15].
Study population COVIP recruited patients with proven COVID-19, defined as a positive polymerase chain reaction (PCR) test result, aged 70 years or older who were admitted to an ICU. Data collection started at ICU admission. Thus, data about pre-ICU triage were not available. The admission day was defined as day one, and all consecutive days were numbered sequentially from the admission date. The dataset contained patients enlisted to the COVIP study from 19 th March 2020 until the 4 th of February 2021.
Demographic data (age, sex, frailty, co-morbidities) The study included 2,646 patients in total, of which 1,480 patients (56%) did not take paracetamol up to ten days prior to admission to an intensive care unit, whereas 1,166 patients (44%) confirmed paracetamol intake prior to ICU admission. The median age was 75 years in both groups (IQR 72-79 years, p > 0. 98); significantly more women reported an intake of paracetamol 10 days prior to ICU admission. Patients on paracetamol intake were slightly more frail in comparison to patients without paracetamol intake (IQR 2-5 versus 2-4, p < 0. 001). In addition, patients using paracetamol had more comorbidities, such as arterial hypertension and diabetes mellitus in comparison to non-users (43% versus 34%, p < 0. 001 for diabetes mellitus and 70% vs 65%, p = 0.
Regarding paracetamol intake, we did not assess and document the doses of paracetamol ingested. Limitations of care in the pandemic surges did not allow to measure plasma metabolites and estimate true drug exposure, thus limiting data reliability. Lastly, our study did not analyze the intake of non-steroidal anti-inflammatory drugs, such as ibuprofen or aspirin and their corresponding impact on mortality and clinical course. Therefore, no recommendation towards a specific subgroup of analgesics can be made.
time plot. Active uptake CLint is then indirectly calculated by subtracting the passive uptake CLint (see below) from the total uptake CLint. We recommend no fewer than three data points (preferably conducted in duplicate) to ensure confidence in the estimated slope. Conducting initial uptake studies can be a challenge since, for some transporters (such as OATPs), linear drug uptake occurs over a short duration such as 5-10 seconds (V. Kumar, Yin, et al. , 2020). In this case, data at only two time points may be all that can be obtained to estimate uptake CLint. Use of a single time point is not recommended as it entails assuming that the nonspecific binding of the drug (i. e. intercept) is negligible. Also, such short incubation times can result in increased technical variability due to the time needed to process the sample after quenching the uptake.
Extensive binding to the cell surface will result in over-estimation of the uptake CLint of the drug. Currently, there is no consensus on the number of washes (but usually 2-3) nor the volume to be used (but usually bigger than the incubation volume)  (Izumi et al. , 2022; Kimoto et al. , 2017a; V. Kumar, Li, et al. , 2020; V. Kumar, Yin, et al. , 2020; Miyauchi et al. , 2018; Sachar et al. , 2020; Watanabe et al. , 2011). Both in-house (unpublished) and published data suggest that the number of washes can have impact on the estimate of drug uptake  (Yoshikado et al. , 2021). Also, to reduce non-specific binding of the drug, some add bovine serum albumin to the washing buffer  (Niessen et al. , 2009). Of note, uptake into suspended hepatocytes, measured using the oil-spin method, does not involve any washing step.
Estimation of efflux CLint using membrane vesicles Membrane vesicles are useful to study efflux transport by the ATP-binding cassette transporters (e. g. Pgp, BCRP, MRP2/3) because a significant fraction of the membrane vesicles is in the inside-out configuration (Fig. 2B ), allowing the transporter cofactor (ATP) in the assay buffer to directly interact with the cofactor binding site. As a result, data analysis is comparable to that of uptake assays. Active transport is determined by subtracting the drug uptake into vesicles in the presence of adenosine monophosphate (passive uptake) from drug uptake in the presence of adenosine triphosphate (total uptake). Passive drug uptake can also be determined using mock vesicles and adenosine triphosphate. To use vesicles for IVIVE of the ER or efflux CLint, the percentage of inside-out vesicles in the vesicles must be determined (e.
Predictive performance of IVIVE approaches for transporter-based CL and tissue dug concentrations Here, we summarize the performances of the different scaling approaches described in Section 4 to predict tissue distribution and hepatic/renal CL of transported drugs. Because there are limited data on the validation of IVIVE of drug absorption mediated by transporters, this aspect is not discussed here. It is noteworthy to mention that success of IVIVE approaches can vary between different studies, based on three aspects: (i) the available validation dataset (e. g. , systemic PK data, PET imaging, PK/PD modeling); (ii) the parameter used for validation (e. g. , systemic or tissue concentration-time profiles, whole organ CL, uptake/efflux CLint, and Kp,uu); and (iii) the acceptance criteria used (predicted parameter falling within boundaries that range from 1.
In principle, this dynamic approach enables incorporation of inhibiting/inducing metabolites, dose staggering, concomitant use of multiple inhibiting drugs, net effect of inhibition and induction, and interplay of multiple enzymes and transporters. A summary of research articles that have used PBPK modeling to predict transporter-based DDIs is provided in Table  5. In most cases, the transporter-based CLint of victim drugs was either estimated from systemic PK data or incorporated an ESF applied to the in vitro-determined transporter-based intrinsic CL. In addition, the inhibition potential of the perpetrator (Ki, IC50) was optimized from that experimentally determined in vitro to best recapitulate the extent of DDIs observed in humans. Also, in many cases, there was no full characterization of the contribution of different transporters to the transporter-based CL (e.
, 2013; Gerk et al. , 2004; Izumi et al. , 2013 ) may occur. In several studies, the victim drugs used in vitro were probe substrates of transporters, which were not the target victim drugs in the DDI prediction. If the inhibitory capacity of perpetrator is substrate-dependent, this will result in a discrepancy in the in vitroin vivo translation of DDI with the target drugs. Thus, if possible, to avoid bias due to substrate-dependent inhibition, in vitro inhibition data should be obtained for the clinically relevant perpetrator-victim pair. Therefore, a harmonized in vitro experimental design and precise acceptance criteria should be considered for accurate prediction of transporter-based DDI.
Acknowledgement The authors acknowledge support from the University of Washington Research Affiliate Program, funded by Gilead Sciences, Amgen, Takeda and Janssen, the National Institute of Health (P01 DA032507, R01  Passive diffusion CL of the drug can either be estimated by fitting a Michaelis-Menten model, incorporating both saturable (active) and non-saturable (passive) components, to the observed concentration-dependent data (panel B) or by fitting a linear model to the data obtained by one of the methods illustrated in panel C (applicable for both types of assays). That is, uptake determined using a) mock cells/vesicles not expressing the transporter of interest; b) transfected cells/vesicles or primary cells co-incubated with an inhibitor(s) at a concentration that completely inhibits the transporter(s); c) transfected cells/vesicles or primary cells incubated at 4 °C (on ice); d) self-inhibition (i.
ASSOCIATED CONTENT Supporting Information. The supporting information is available free of charge via the Internet at http://pubs. acs. org. Experimental procedures, chiral HPLC and GC chromatograms, NMR spectra of the prepared compounds, cell-based assays, computational and SCXRD details, photos of mechanically responsive behavior (PDF). Movie 2d (AVI) Movie 4d (AVI) Accession codes. CCDC 2151748-2151755 and 2155509 contain the supplementary crystallographic data for this paper. The data can be obtained free of charge from The Cambridge Crystallographic Data Centre via www. ccdc. cam. ac. uk/structures.
ACKNOWLEDGMENT This work was supported by the  Slovenian Research Agency ARRS , Grant No. P1-0208 ,  Z1-2635 ,  J1-1717 ,  Centre National de la Recherche Scientifique CNRS, Normandy University, French-Slovenian bilateral  grant  BI-FR/22-23-PROTEUS-004 , and  Los Alamos Laboratory Directed Research and Development. M. L. gratefully acknowledges the funding by the  European Research Council  ( 950625 ). T. T. acknowledges  OpenEye Scientific Software ,  Santa Fe , NM. , for free academic licenses for the use of their software. Maja Frelih  is acknowledged for acquisition of HRMS spectra.
Serum concentrations following single IV administration of 10 mg/ kg RIF in male CD1 mice (20-27 g) were included to support estimation of the disposition parameters in the RIF PBPK model. Details of the experimental protocol can be found elsewhere  (Bruzzese et al. , 2000). In addition, as in vivo lung tissue concentrations of PZA were not collected in the initial experiments, predicted lung tissue/plasma ratio was validated against published whole lung tissue/plasma ratio measured in female BALB/c mice following single administration of 150 mg/kg PZA via oral gavage. We have assumed that PZA disposition was not significantly different between BALB/c and C57BL/6J mice. Full details of the experiment can be found elsewhere  (Irwin et al. , 2016). Plasma and lung concentrations were digitized from the publication and were added to the data set for the PBPK model building.