dataesr/scientific-paragraphs-categorization huggingface dataset:
If no data were available, an age range of 20-50 years was assumed. Table  S1  provides the geometric standard deviations used for the modeled variation of the relevant transporter and enzyme concentrations. Unknown model parameters and parameters with a high impact on the results of permeability and partition quantitative structure-activity relationship (QSAR) models of PK-Sim® were fitted using the training dataset. Oral formulations differing in release kinetics were incorporated via separate Weibull functions (Equation  1 ), with the time to 50% dissolution and shape as model input parameters. In a stepwise approach, tacrolimus model parameters were first optimized based on intravenous and oral administration of immediate-release (IR) capsules (Prograf®). Next, the model was adjusted to extended-release (ER) capsules (Advagraf®) by estimating the respective release where f d (t) = fraction of administered dose dissolved at time t, exp = exponential function, t lag = lag time between drug intake and the start of the dissolution process, b = shape parameter, and a = scale parameter.
PBPK model evaluation The final tacrolimus model was evaluated both graphically and statistically. For visual comparison, predicted whole blood concentration-time profiles were plotted alongside their corresponding observed data points. Goodness-of-fit (GOF) plots were generated to examine the deviation of predicted to observed concentration measurements. Moreover, GOF plots were used for comparison of the calculated AUC determined between first and last concentration measurements (AUC last ) and maximum whole blood concentration (C max ) values for all predicted versus observed profiles. Predictions within the twofold range of observed values were considered successful. Statistical evaluation was conducted via calculation of mean relative deviations (MRDs) for all predicted concentrationtime points (Equation  2 ) as well as geometric mean fold errors (GMFEs) for predicted AUC last and C max values (Equation  3 ).
DD(G)I modeling The influence of CYP3A perpetrators on tacrolimus PK was investigated by coupling the tacrolimus model with previously published models of voriconazole, itraconazole, and rifampicin. 27, 28 The respective model files were downloaded from https://github. com/Open-Syste ms-Pharm acology. PK-Sim® Version 10 model files for itraconazole and rifampicin were obtained from https://github. com/ Open-Syste ms-Pharm acolo gy/Itrac onazo le-Model/ relea ses/tag/v1. 3 and https://github. com/Open-Syste ms-Pharm acolo gy/Rifam picin -Model/ relea ses/tag/v1. 2. Relevant interaction parameters were incorporated from the literature according to the respective types of interaction reported, that is, induction, competitive inhibition, and mechanismbased inactivation, as described in Section S4.
PBPK model building and evaluation The tacrolimus PBPK model was built and evaluated using a total of 37 whole blood concentration-time profiles (summarized mean values with variance information if available) obtained from 25 clinical studies for the training and test datasets. Included were five intravenous SD applications of 0. 015-0. 025 mg/kg body weight (BW) tacrolimus as well as 32 oral administrations of therapeutic doses ranging from 0. 5 to 10 mg. IR and ER formulations of tacrolimus accounted for 27 (SD) and five (SD and multiple dose [MD]) of the profiles used, respectively. Information on all profiles, including the frequency of the CYP3A5 *1 allele for each study population, is provided in Table  S2. The metabolism of tacrolimus was implemented via CYP3A4 and CYP3A5, with K M values adopted from the literature and k cat values optimized during the modelbuilding process.
A summary of the key modeling assumptions, including the resulting modeling decisions, is available in Table  S4. The tacrolimus PBPK model file can be found in Appendix S2. Overall, the final tacrolimus model showed good descriptive (training dataset) and predictive (test dataset) performance. A representative sample of whole blood concentration-time profiles from both the training and test datasets is provided in Figure  1. Semilogarithmic and linear plots of all predicted and observed profiles are listed in Sections S2. 1-S2. 2. Figure  2  displays GOF plots of predicted versus observed concentration measurements as well as AUC last and C max values stratified by the training and test datasets. A total of 96% of all predicted concentration measurements as well as 37/37 of predicted AUC last and 37/37 of predicted C max values were simulated within twofold of their corresponding observed data.
CYP, cytochrome P450. based on the mode of the respective study participants' race and ethnicity. 14 However, as CYP3A5 genotyping had not been performed in the majority of included studies, this lack of information might have led to biases in the modeling and estimation processes. Here, a larger and complete PK dataset on specified CYP3A5 genotypes might be required to further improve and refine the genotype-specific modeling of tacrolimus exposure. The same applies to CYP3A4 variants, which have not been considered so far.
41, 42 ther investigators used a minimal PBPK model of tacrolimus to also investigate the impact of CYP3A5 genotype on tacrolimus PK in addition to other factors such as hematocrit. 43 Due to considerable structural differences between whole-body and minimal PBPK models, significant differences in the observed study populations, and differing implementation of CYP3A5, a comparison of the two models is difficult. Moreover, due to the minimal overlap of estimated and used model input parameters as well as vast differences in cardinality of training and test datasets, comparing the predictive model performances might not be reasonable. The presented tacrolimus model provides new important insights by investigating the food effect of tacrolimus as well as clinically relevant DD(G)Is involving CYP3A perpetrators, particularly azole antifungal agents, while considering different CPY3A5 activities.
Downloaded from https://ascpt. onlinelibrary. wiley. com/doi/10. 1002/psp4. 12946 by Universitaet Des Saarlandes, Wiley Online Library on [07/06/2023]. See the Terms and Conditions (https://onlinelibrary. wiley. com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License F I G U R E 1 Representative plots of whole blood concentration-time profiles of tacrolimus. Stratified by training (a-f) and test (g-l) dataset, solid lines and ribbons represent population predictions (n = 1000; geometric mean and geometric standard deviation), whereas corresponding observed data are shown as dots (± standard deviation, if available).
Goodness-of-fit plots of the final tacrolimus model. Stratified by training (left column) and test dataset (right column), predicted whole blood concentration measurements (a-b) as well as AUC last (c-d) and C max (e-f) values are plotted against corresponding observed data. The solid line represents the line of identity, whereas dotted lines indicate 1. 25-fold and dashed lines twofold deviation from the respective observed value. Detailed information on all investigated profiles is provided in Table.
CONFLICT OF INTEREST STATEMENT Donato Teutonico is an employee of Sanofi and uses Open Systems Pharmacology software, tools, or models in his professional role. Donato Teutonico and Thorsten Lehr are members of the Open Systems Pharmacology Management Team. Sebastian Frechen uses Open Systems Pharmacology software, tools, or models in his professional role and is a member of the Open Systems Pharmacology Sounding Board. All other authors declared no competing interests for this work.
21638306, 2023, 5 , 5 Downloaded from https://ascpt. onlinelibrary. wiley. com/doi/10. 1002/psp4. 12946 by Universitaet Des Saarlandes, Wiley Online Library on [07/06/2023]. See the Terms and Conditions (https://onlinelibrary. wiley. com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License F I G U R E 1 Representative plots of whole blood concentration-time profiles of tacrolimus. Stratified by training (a-f) and test (g-l) dataset, solid lines and ribbons represent population predictions (n = 1000; geometric mean and geometric standard deviation), whereas corresponding observed data are shown as dots (± standard deviation, if available).
F I G U R E 2 2 Goodness-of-fit plots of the final tacrolimus model. Stratified by training (left column) and test dataset (right column), predicted whole blood concentration measurements (a-b) as well as AUC last (c-d) and C max (e-f) values are plotted against corresponding observed data. The solid line represents the line of identity, whereas dotted lines indicate 1. 25-fold and dashed lines twofold deviation from the respective observed value. Detailed information on all investigated profiles is provided in Table.
Event generation Cross section or width calculations and event generation require the application of several high-energy physics packages. For implementing the theoretical simplified models, the framework FEYNRULES  [18]  is used in order to extract in an automated way the Feynman rules and to store them in terms of couplings and parameters into the UFO (Universal FeynRules Output) format  [19]. This model is then interfaced to the Monte Carlo (MC) generator MADGRAPH AMC@NLO  [20]  in order to generate the hard process at the Leading-Order (LO) of the Quantum ChromoDynamics (QCD). No kinematics or geometrical cut is applied at this stage. The NNPDF30  [21]  Parton Distribution Functions (PDF) at LO QCD is used for the generation, and is embedded in the LHAPDF package  [22]. At this point, the produced events contain only leptons and partons.
The magnetic field has also been taken into consideration by taking a uniform value of B=3. 8 T aligned with the beam axis (zaxis), which corresponds to the one of the CMS detector 1. The simulation also benefits from one of the last developments  [30]  of MADANALYSIS 5 which allows us to apply the magnetic field to all electrically charged particles, including charged long-lived particles. Then, the jets are reconstructed using the FASTJET package  [31]  with an antik T algorithm  [32]  according to the following parameters: p T > 30 GeV with p T the transverse momentum, η < 2. 5 and ∆R = ∆η 2 + ∆φ 2 < 0. 4 (where ∆η is the differ- 1 Note that even though the magnetic field of the ATLAS detector is different, the tracking specifications between both experiments can be considered similar at the accuracy level of this phenomenological work.
Acknowledgments The authors are grateful to the  CMS  and ATLAS collaborations for the interesting discussions about this perspective work. We would like to thank also  Gilbert Moultaka  and  Michel Rausch de Traubenberg  for their precious help on the GMSB and the supergravity models. We cannot conclude without thanking  Benjamin Fuks  and  Jack Araz  for their valuable developments within the MADANALYSIS 5 framework.
Small-angle X-ray scattering experiments A collection of small-angle X-ray scattering (SAXS) measurements of the H 3(1-x) M 3x systems was conducted at the Swing SAXS beamline of the SOLEIL synchrotron facility at Saint-Aubin, France. As previously reported, the X-ray energy was 12 keV corresponding to the wavelength λ = 0. 1033 nm. The sample-to-detector distance was 6. 226 m, so that the scattering vector modulus, q (q = (4π sin θ)/λ where 2θ is the scattering angle) ranged between 10 -2 and 0. 5 nm -1. The beam size was 375 × 75 µm 2 at the sample level. The scattering patterns were recorded with an Eiger-4 M detector and the exposure time was typically 0. 5 s. The usual SAXS data reduction procedures were made, using the Swing data reduction software (Foxtrot 3. 4. 9), and the data was displayed either as 2-dimensional SAXS patterns or as plots of the scattered intensity versus scattering vector modulus, I(q), obtained by azimuthal averaging of the SAXS patterns.
Acknowledgements We acknowledge  SOLEIL  for provision of synchrotron radiation facilities (under the approved proposals # 20190467  # 20191221  & # 20201118 ). We thank  Thomas Bizien  and  Stéphan Rouzière  for help with the X-ray scattering experiments (SOLEIL and tests at MORPHEUS Platform of LPS, respectively) as well as (i) middle school intern  Alistair J. Gabriel  for our group's first synthesis and observations of H 3(1-x) Li 3x Sb 3 P 2 O 14 samples; (ii) Engineering school third year interns  Pan Zhou  and  Fatima Hami  for preliminary results on other H 3(1-x) M 3x Sb 3 P 2 O 14 synthesis. LC, KER, LM, PD & JCPG acknowledge financial support from the " Agence Nationale de la Recherche " Grant no. ANR-17-CE04-0003 , project  4WATER. LC and JCPG acknowledge CEA's  Nanoscience program  financial support, project  NANOFILTRE.
Cells were seeded at a concentration of 5,000 (HeLa) or 5,600 (IMR90 cells) per well in 100 µL culture medium containing the test compound (Compound 3-3, amphotericin B or fluconazole) into 96 wells microplates (Falcon ref. 353072). Plates were incubated at 37 °C and 5 % CO2 for 24 h before adding 10 mL of MTT labelling reagent (final concentration 0. 5 mg/mL) to each well. After incubating for a further 4 h, 100 m-L of the solubilization solution were added in each well. Plates were allowed to stand overnight in the incubator before measuring the absorbance at 570 nm and at 690 nm in a ClarioStar plate reader. The values of A570 nm-A690 nm were normalized relative to that obtained with vehicle (0. 2 % DMSO, 0. 8 % ethanol) and plotted against compound concentration. 3 RMSD values were calculated by program lsqkab of the CCP4 suite 55 using the Ca positions of the aligned residues indicated.
DESIGN, SETTING, AND PARTICIPANTS This was a nationwide retrospective cohort study based on data linking between 2 prospective French nationwide registries. Clinical transplant parameters, outcomes, and RBCT characteristics were extracted from the CRISTAL registry of the Agence de la Biomédecine and the national database of the Etablissement Français du Sang. All 12 559 patients having received a first kidney transplant in France between January 1, 2002, and December 31, 2008, were included. Patients were followed up from transplant to graft loss, death with a functional graft, or data retrieval in June 2016. Data were analyzed from April 2019 to June 2022. EXPOSURES Clinical outcomes of transplant recipients who underwent early RBCT were analyzed considering transfusion characteristics.
Cumulative incidence function estimate with subdistribution hazards approach proposed by Fine and Gray was used to model the association of parameters with graft loss-free survival and survival with functional graft. All analyses were performed using SAS version 9. 4 (SAS Institute) and R version 4. 1. 2 (R Foundation). P values less than. 05 were considered statistically significant, and all tests were 2-sided. P values were not corrected for multiple tests and should be interpreted as exploratory except for the primary objective (the association between RBC storage duration with transplant failure-free survival). Details on the interpretation of important statistical concepts are given in the eMethods in Supplement 1.
Population and RBC Units Our database identified 3483 of 12 559 (28%) patients who received an early posttransplant RBCT. Most had 1 transfusion episode (n = 2512 [72%]), most often consisting of 1 or 2 RBC units (n = 166 [7%] and 1739 [69%], respectively) (eFigure 1 in Supplement 1). Patient characteristics have been previously described. 10.
Detailed patient characteristics and blood product characteristics for each unit of transfused blood were extracted from national databases. This study was approved by all French regulatory authorities (Committee for Personal Protection Great East II in Besançon, Advisory Committee on Information Processing for Research in the Field of Health, and French National Computers and Freedom Commission). The exemption of written informed consent was guaranteed by the Advisory Committee on Information Processing for Research in the Field of Health. The study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.
JAMA Network Open | Nephrology Transfusion within 14 days posttransplant was completed for each first kidney transplant graft betweenJanuary 1, 2002, and December 31, 2008. Demographic and clinical data were extracted from the national kidney transplant database CRISTAL. Patients were followed up from inclusion on list to graft loss, death, or data retrieval in June 2016. Early graft failures, within 15 days posttransplant, were excluded from analysis. All RBC concentrates were produced by the Etablissement Français du Sang and underwent prestorage deleukocytation (<1 × 10 6 leucocytes/RBC unit). Data collected included blood donor demographic characteristics (including age and sex) as well as blood product characteristics for each RBCT. A transfusion episode was defined as consecutive transfusions whose interval did not exceed 48 hours.
Recent studies have proposed a new method to analyze the injury profile from a stratified road-accident victim database, through the computation of multiple statistical injury associations  [12]. Such a method allows to obtain the conditional probability (odd-ratio) of observing a given injury knowing that a specific one is present and to graphically represent these conditional dependencies between them. This method can be applied to stratified data, i. e. data separated according to predefined categories, such as type of road users, as illustrated in the cited article. This model can be used to retrieve injury profile and injury associations patterns with respect to the different strata. The present study proposes an investigation of the influence, in terms of injury mechanisms, of all the injury associations related to car-accident front passengers and drivers, according to the type of accident.
Epidemiological databases This study relies on two data collections of road traffic casualties with complementary information: • The Rhône Registry of Road Traffic Casualties Since 1995, all victims of road traffic crashes that occurred within the Rhône county administrative area and who benefited from medical care in public or private facilities are included in this registry. A more detailed description of the cohort protocol is available elsewhere  [13]. The Rhône registry data collected in medical care units include a precise description of injuries as well as general information about crash circumstances and victims' characteristics. This registry has been approved by the French National Registry Committee. • The French national police database of road traffic accidents For each road crash involving at least one vehicle and with at least one injured casualty, French police forces are required to complete a police report.
These reports are then compiled yearly to create a national crash database used for French road traffic crashes monitoring. This database includes for each victim rather precise information about the crash, vehicles and people involved. Contrary to the Rhône Registry, there is little information about injuries. More specifically for car crashes, it includes the accident type (frontal, lateral, rear, multiple crash events) and the location of the victim inside the car (driver, front or back passengers), information not available in the Rhône registry. For the present study, data from these two databases were merged to obtain a complete database with a full description of injuries and car crash types. All methods were carried out in accordance with relevant guidelines and regulations in the "Ethics approval and consent to participate" section.
Injury classification Two injury classifications were used: • The Abbreviated Injury Scale (AIS) For a full description of injuries, the Abbreviated Injury Scale (AIS) 1990 was used  [14] , which is an internationally recognized traumatic injury scoring system. The AIS includes codes consisting of 6 digits that refer to body regions (R, first digit), types of anatomic structure (T, second digit), additional specifications of anatomic structure or location (S, third and fourth digits), and the specific type of lesion (N, fifth and sixth digits). For example, the code 440604 correspond to R: Thorax (4), S: Internal organs (4), T: Diaphragm (06) and N: Wound (rupture) (04), and therefore represents a wound or rupture injury in the diaphragm which is an internal organ in the thorax region. The Rhone registry merged with the French national police dataset contains 1348 distinct codes corresponding to 1348 distinct injuries.
Data stratification Previous studies have shown that different impact principal directions of force (PDOF) lead to different injury mechanisms, patterns and severity  [6]. As a consequence, the assumption was made that the injury associations consecutive to each type of accident will as well be different. Stratification of data is made in order to divide the data into clusters that are hypothesized to display a certain homogeneity within the strata and heterogeneity with respect to the others. Consequently, we stratified the data according to four types of accident present in the Police Road Crash database: frontal, lateral, rear, and multiple crash events. Multiple crash events were removed because of a too small sample size available, leading to a weak statistical power to evidence injury associations.
Data selection In the merged database, a different version of the AIS was used for 2015 and subsequent years. In order to keep the largest sample size with homogeneous injury coding for frontal and lateral comparison, only casualties of 2014 and before were kept. We then selected all the victims of car crashes, positioned in the front row (passengers and drivers) and whose type of accident is known. This led us to work with 15,658 observations (13,097 and 2561 observations in the frontal and lateral stratum respectively).
The diagram on the left hand side shows the common associations between lateral and frontal impacts. The two diagrams on the right hand side display the associations that were statistically significant specifically for the frontal (top) and lateral (bottom) impacts, apart from the common associations shown on the left hand side of the figure. In the frontal case, a total of 50 injury associations were found against 34 for the lateral one. This may be due to fewer lateral cases versus frontal ones in our dataset, and thus less statistical power to highlight injury associations. Among these associations, 19 are specific to frontal impacts and 3 to lateral impacts. 31 associations are common to both strata,so most of the associations are common to the two types of crash. A larger number of crash-specific association is found in the frontal case compared to the lateral case, most probably due to a higher number of cases available and consequently a higher statistical power.
However, it prevents to evaluate association between unique injuries from the same type of tissue within the same body region. Without the RTSN classification, the identification of the clavicle fracture as possible index to help paramedics and doctors in their diagnosis and decision-making process would not have been feasible. This highlights the complexity of injury associations and patterns, and the pertinence of having a two-scale approach when studying injury associations: a global approach with the BC to point out the relationships between body structures, and a detailed approach (using the RTSN classification) allowing a precise identification of the wounds. This study suffers from certain limitations. First, the PDOF was only described through 4 categories of accident type in our database, which may oversimplify the exact PDOF.