dataesr/scientific-paragraphs-categorization huggingface dataset:
External Validation Dataset Cohort The EVD included 116 PK samples from 69 patients with obesity taking apixaban for VTE treatment, and 121 PK samples from 81 patients with obesity taking rivaroxaban  [16]. The median and range of the age and weight of each subpopulation are presented in Table  1. All the patients were sampled after at least one month of treatment. Since the genotypes, HCT, comedications, ALT, and BUN were not collected in our dataset, the values of the parameters affected were arbitrarily set either to median population values to cancel the effect of the covariate, or to credible values for the population (that is ALT = 22 UI/L, HCT = 0. 4 for females, HCT = 0. 45 for males).
The two-compartment models best described the pharmacokinetics of apixaban in the external validation dataset. Models A2 and A4 were based on studies that used both intensive and sparse sampling and described the PK of apixaban in the EVD satisfactorily. The mass descriptor included in models A2 and A4 was Total Body Weight (TBW), similar to the all-apixaban PPK models analyzed, and the influence of renal function on apixaban PK was included using the Cockcroft formula (model A4), or the Cockcroft formula limited to 150 mL/min (model A2). Because these approaches were common to all the apixaban models analyzed, it was not possible to conclude whether other descriptors of weight (e. g. , Lean Body Weight (LBW), adjusted weight) or renal function (e. g. , serum creatinine, CKD-epi) would be of any value.
An additional limitation was the lack of information in the dataset regarding some of the covariates in the published models. The introduction of bias due to the imputation of missing variables (HCT for models A4 and R4, BUN for model R12, pharmacogenomics for models A5 and R15) cannot be excluded. Among these covariates, race is a special case (models A1, A2). Firstly, the collection of ethnic statistics is not allowed in France, and, secondly, the concept of race is questionable and an inadequate descriptor of the distribution of genetic variability in our species  [41]. In the specific context of PK, a considerable amount of literature comparing PK between ethnic groups has been produced, with more papers reporting similarities than differences, and a decrease in the proportion of papers showing ethnic differences over time  [42].
Overall, these data suggest disregarding racial covariates. The EVD anticoagulant assay could also be questioned, but the correlation between drug-calibrated anti-FXa methods and LC-MS/MS has been demonstrated for routine concentrations  [43] , and the satisfactory performance of some apixaban PPK models developed from LC-MS/MS data (A2, A4) is reassuring. Finally, as all the models were implemented in Monolix software, the implementation of the residual error models might have introduced additional imprecision in the model evaluations because of its difference with the implementation of NONMEM  [44]. In light of the reassuring clinical data, the latest ISTH guidance suggested using standard doses of apixaban or rivaroxaban in the treatment of VTE, regardless of patient weight or high BMI  [14].
Since our last literature search, new pharmacokinetic studies have been conducted or are underway. For example, a Pubmed/MEDLINE search in February 2023 using the keywords "rivaroxaban population pharmacokinetics" for 2021-2023 identified five studies  [45] [46] [47] [48] [49]  that would have met our systematic review inclusion criteria by title and abstract. All were single-compartment models with first-order uptake, and none were specifically designed for the obese patient population. The present study did not evaluate these models from studies conducted after our last literature search, but an ongoing phase 1 study aiming to assess the PK of rivaroxaban used as a therapeutic anticoagulant dose in patients with previous bariatric surgery, and in morbidly obese subjects (NCT04180436)  [50] , will provide the data necessary to develop an adequate PPK model in this population.
This group was characterized by one or more of the following symptoms: pulmonary edema, acute respiratory distress syndrome, hemoglobinuria, acute kidney failure, abnormal liver function, or severe anemia (hemoglobin (Hb) levels under 5 g/dL). Both the CM and SNCM groups included children with or without severe malarial anemia, a common pheneomenon with severe forms of malaria. With permission, 2 to 4 mL of venous blood were collected into citrate phosphate dextrose adenine (CPDA) tubes. The plasma from the blood samples was then stored at -80 • C for the ELISA test after 10 min of centrifugation of the whole blood at 1500× g at 4 • C. Once collected, patients' personal and clinical data were included in an ad-hoc data form on MS-Excel. The data was anonymized by replacing individual names with unique codes for privacy, thus, preventing bias in data analysis.
Acknowledgments: We would like to acknowledge all the children who participated in this study and their parents/guardians for their support. We are grateful to all the medical staff of  Centre Hospitalier Universitaire Mère-Enfant la Lagune (CHUMEL) ,  Centre National Hospitalier Universitaire Hubert Koutoucou Mega (CNHU-HKM) , and  Centre Hospital Universitaire of Suruléré (CHU Suruléré) in Cotonou, Benin  for their contributions to this work.
Genetic factors HLA typing was performed by DNA sequencing of loci A, B, C, and DQB1 and 2-4 exons of the DRB1 gene using the HLA holotype kit (Omixon, Budapest, Hungary). Briefly, the isolated DNA concentration was estimated using an Implen NP80 NanoPhotomer (Fisher Scientific, Pittsburgh, PA), and long-range PCR was performed next with the resulting amplicons of the five loci. Then, in the process of library preparation, the amplicon pools were fragmented, ends were repaired, and barcodes were ligated. Finally, the sequencing was performed on a MiSeq nextgeneration sequencing (NGS; Illumina, San Diego, CA) instrument in the pair-terminal reading mode. The readings obtained (251 pb) were analyzed using the HLA twin software (Omixon) with two algorithms in a day-by-day assembly mode and by mapping against the global international ImMunoGeneTics (IMGT) database.
The main limitations of this study are its retrospective design with incomplete data sets and a database initiated before the CSA score was created. In particular, factors with a low rate of inclusion (<50%) have to be considered with caution. On the other hand, advantages are related to the homogenous genetic background reported between pre-RA and FDR individuals, the elevated number of pre-RA individuals included, and the delay of ≥2 years in the follow-up to exclude the pre-RA stage in both pre-RA and FDR groups. However, we could not firmly exclude that some individuals from the FDR subgroup would evolve to RA and that environmental risk factors have changed from inclusion to RA development such as childlessness and rural location. In conclusion, a set of clinical, individual, and genetic characteristics to predict RA development in relatives from familial RA was established.
Data collection We used the data collection platform "Form Assembly" to build the survey and to collect the data. The survey was built as a closed survey and the results were only accessible to the investigator. To keep the questionnaire streamlined, we used some simple conditional questions. It was optional to add personal information. It was only possible to send the completed questionnaire once, but respondents were able to review and change their own answers. The IP address of the respondents was used to identify potential duplicate entries from the same user. If there were duplicates with a complete and an incomplete questionnaire, the incomplete one was removed. The completion rate of the individual questionnaire was 82% and of the team questionnaire 86%. We analysed complete as well as incomplete questionnaires.
General After clearing 9 duplicates, 493 responses from 71 countries were analysed (Supplement Fig. 1 ). The completion rate was 82%. Most of the missing data from the 90 incomplete datasets were with regard to hospital type and profession. For the team survey, from 287 invited sites we received 95 replies (response rate 33%). Most of the participants were surgeons (individuals: 47%; team responders: 45%) and most were employed at a university or teaching hospital (60% of individuals, 73% of teams), followed by regional hospitals (15% versus 14%), specialized hospitals (6% versus 9%) or other institutions (1% versus 0%), mostly private hospitals. Specialized hospitals were defined as hospitals providing specialized care (e. g. oncology, orthopedic surgery) not related to AMI. Overall, there was a high percentage of European and especially Italian teams (32%) participating.
Although the recently completed SENECA trial [  (7) , NCT02509156], which entailed a single catheter-based endomyocardial injection of allogeneic MSCs, did not meet its primary end point, it also reported some encouraging efficacy signals. Although cell transplantation has been initially conceived as a "replacement" therapy whereby grafted cells would structurally engraft in the myocardium and replace dysfunctional cardiomyocytes, there is now mounting evidence that cells exert their beneficial effects through paracrine signaling mediated by the cellular secretome whose content is largely packaged in proteolipid bilayered extracellular vesicles (EV). These particles are able to transfer their biologically active cargo (composed of proteins, lipids and coding and non-coding nucleic acids) into recipient cells through membrane fusion, endocytosis or ligand/ receptor binding, thereby harnessing endogenous repair pathways in the target cells  (8, 9).
Proteomic analysis of EV-CPC The proteomic cargo of EV-CPC was processed and analyzed as extensively described in Supplemental Material. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD022129 (Username: reviewer_pxd022129@ebi. ac. uk, Password: Z8DFhSCS). In brief, immune-competent BALB/c mice received 3 weekly intra-peritoneal (IP) injections of doxorubicin (DOX; 4 mg/kg each; cumulative dose: 12 mg/kg), were then intravenously (IV) injected three times with EV (total dose: 30 billion) over 2 weeks and finally assessed 9-11 weeks later by cardiac magnetic resonance imaging (FLASH cine sequences on a 4. 7 T preclinical Bruker scanner) (Figure  1A ). Absolute values of Circumferential Strain (CS) and Longitudinal Strain (LS) measures (Tissuetracking module, Circle Cardiovascular Imaging software, Biomedical Imaging Research Laboratory CREATIS, Lyon, France) are expressed both as a percentage and in percentage change (%Δ) from mean baseline values set at 100%.
EV-CPC improve heart contractility in CCM mice To validate the ability of our DOX regimen to induce a cardiomyopathy, end-study data recorded in DOX-injected mice were compared with baseline values collected at the onset of the protocol in 5 healthy mice. Compared with these normal mice, DOX resulted in a significant impairment of Global Longitudinal Strain (GLS; -13. 35 vs. -14. 80, p ≤ 0. 046). Likewise, compared with sham-operated (DOX-free, saline-injected) mice, those injected with DOX incurred declines in diastolic mass and ejection fraction, an increase in LV volumes (Supplementary Figures  S1A-E ) and a QTc interval lengthening with a decreased cardiac output (without changes in heart rate) (Supplementary Figures  S1F-J ). Similar trends were observed when papillary muscles were included in the segmentation of the inner diameter (Supplementary Figures  S2A-F ).
To adjust for the variability in the response of rats to chemotherapy, end-study data are presented as percent changes from those recorded at day 10, i. e. , the end of DOX treatment, and allocation of rats to GMP-EV of placebo injections was then made as to ensure their comparability at this time point. While LVESV volumes were significantly increased in placebo-injected hearts compared with DOX-untreated sham (p = 0. 033), they were preserved by GMP-EV injections (effect size Cohen's d index of 0. 4) (Figures  4A, B ). Likewise, the percentages of "responder" rats which did not increase their LVEDV volumes by more than 5% from their post-DOX pre-treatment values were 58% (7 out of 12) vs. 28% (3 out of 11) in GMP-EV-and placebo-injected hearts, respectively (effect size Cohen's d index of 0.
Funding This work was supported by the  Fonds Marion Elizabeth BRANCHER, France  ( Doctoral Grant  to MD); the  Fondation de l'Avenir  ( AP-RM-18 017 ); the  French Muscular Dystrophy Association, France  ( EVEDOX  # 22378 ); the  LabEx REVIVE  ( ANR 10-LABX-0073 ). This work was partly performed on a platform member of  France Life Imaging  network (grant  27 ANR-11-INBS-0006 ) and  Infrastructures Biologie Santé (IBISA).
This paper evaluates the degrees of freedom and complexity warranted in temperature-index models to jointly simulate local snow measurements, remotely-sensed snow cover and runoff in mountainous areas. To address this issue, the snow-and ice-accounting routine (SIAR) on top of the GR4J model was developed on a dataset covering 17 mountainous catchments (45 to 3580 km 2 ) in the French Alps and Pyrenees. Model calibration and control were based on streamflow series, fractional snow cover (FSC) computed from MODIS snow products and at least one chronicle of local measurements of snow water equivalent (SWE) acquired in each catchment for the period 2004-2016. SIAR was applied according to an adaptable number of elevation bands and different parametrizations ranging from 11 free parameters (precipitation orographic correction, temperature lapse rate, variation in the temperature lapse rate, snowfall adjustment, rainfall lapse rate, thermal inertia of the snow pack, constant and variable part of the degree-day snow melt factor, degree-day ice melt factor, 2-parameter hysteresis between SWE and FSC), to only fixed parameters.
e. , with the minimum number of free parameters to calibrate) while including internal processes that were deemed necessary. Section 2 presents the catchments and the dataset used for the comparative assessment. Section 3 details the modeling experiment: it describes the SIAR structure tested on top of a well-known rainfallrunoff model, and the proposed evaluation strategy. Section 4 summarizes a systematic sensitivity analysis of the main parameters and components that were included in SIAR before briefly comparing its final structure with different versions of the Cemaneige model. Finally, general conclusions are drawn in Section 5.
Selection of catchments The study domain was the French Alps and Pyrenees. In order to avoid case-specific results, a sample of catchments as large as possible was gathered (Fig. 1 , Table  1 ). The study catchments and periods were selected based on two main criteria: (i) the availability of snow and runoff data for model calibration and control; and (ii) anthropization of the catchments should be limited. The French hydro-power company EDF (Electricité de France) provided us with daily in-situ measurements of snow water equivalent (SWE). These data were acquired by cosmic-ray snow sensors (CRS) over the period 2004-2016 by 19 ground stations in the French Alps and Pyrenees (Table  1 ). In addition, snow cover binary maps were computed at 500 m spatial resolution over the regions of interest for the period 2004-2016.
These maps were based on moderate-resolution imaging spectroradiometer (MODIS) daily images from the Terra (MOD10A1) and Aqua (MYD10A1) satellites  (Hall and Riggs, 2006). Following the method described and assessed in detail in  Ruelland (2020) , the classified snow cover maps from Terra and Aqua were combined before applying temporal and spatial filtering to remove cloud obscuration. The daily runoff data used were extracted from the French hydrological database  (Leleu et al. , 2014 , https://www. hydro. eaufrance. fr) for the period 2004-2016. All the catchments available in the database, and represented by at least one of the SWE stations were initially retained. It was then decided to select only catchments whose observed runoff was nearly natural (e. g. not influenced by dams), with good quality measurements according to the hydrological reports, and less than 10% daily missing values over the period 2004-2016.
Meteorological data A dataset was compiled comprising 381 (respectively 265) precipitation gauges and 322 (207) temperature gauges over the French Alps (Pyrenees) from the Météo-France climate database (https://publithe que. meteo. fr) and the local temperature measurements recorded by the EDF snow gauges. The extracted series were the daily liquid equivalent water depth of total precipitation and the mean daily temperature for each gauge covering the period 1998-2016. Although the spatial distribution of the available meteorological stations is reasonably balanced, high elevations (above 2000 m a. s. l. ), which represent 45% (25%) of the surface area of the catchments selected in the Alps (Pyrenees), are rarely monitored. Yet, ignoring elevation dependency in either the precipitation or temperature inputs leads to notable failures of snow-hydrological models, as shown in  Ruelland (2020).
Note that the incoming equations apply equally when the catchment is divided into regular sized cells (in the fully distributed mode) or a single elevation band (in the lumped mode). Mean areal inputs (P, T, SR) were extracted for each elevation band from gridded P, T and SR datasets. In the present study, the number of elevation bands was adapted to approximate the elevations of the local SWE measurements used for model calibration and control. An adaptable number of elevation bands (EB) of equal area according to the catchment hypsometry was defined as EB = (H max -H min ) / 100, where H represents elevation. EB thus ranged from 16 to 32 depending on the catchment. In each elevation band, the SIAR functions presented in Fig. 2  and detailed in the following sections are applied with a unique set of parameters.
Elevation-dependency adjustment of total precipitation (P) and air temperature (T). When gridded precipitation and temperature datasets interpolated without elevation dependency are used, SIAR first enables forcing data for each EB to be modified based on elevation gradients (PLR and TLR) used as parameters (for more details, see  Ruelland, 2020) , as follows: P i (t) = P IDW i (t) × [1 + PLR 1000 × (y i -y IDW i (t) (1) T i (t) = T IDW i (t) + [ -(TLR + TLR × (SI i (t) -0. 5) × CV) 100 × ( y i -y IDW i (t) ) ] (2) where P IDW i (t) and T IDW i (t) are, respectively, the mean areal precipitation and temperature interpolated based on the IDW procedure in EB i at time step t; y IDW i (t) is the mean elevation of the contributing station weighted by their IDW weights in EB i ; y i is the mean areal elevation extracted from the DEM in EB i ; PLR (in % (km) -1 ) and TLR (in • C (100 m) -1 ) are the precipitation and temperature lapse rates to be calibrated (see Table  2 ); CV is a coefficient of variation due to solar radiation to be applied to TLR (when set to 0, no variation is applied); SI is an index of change in solar radiation and is computed as:.
This approach uses a simple unweighted gradient of the four nearest neighbors for slope and calculates clear sky radiation corrected for the incident angle (terrain shading). Insolation thus depends on the day of the year, and on the latitude, elevation, slope and aspect. Mean areal SR i was extracted for each EB i in each catchment from a 500m gridded SR dataset covering the entire study domain. 3. 1. 1. 3. Potential evaporation. SR  was also used to compute potential evaporation (PE, expressed in mm) in each elevation band using the temperature-based formulation proposed by  Oudin et al. (2005) : PE i (t) = max ( SR i (t) λρ × (T i (t) + 5 ), 0 ) (4) where λ is the net latent heat flux (fixed to 2. 501 MJ kg -1 ) and ρ is the water density (set at 997 kg m -3 ) and T i (t) is the air temperature ( • C) estimated in EB i at time step t.
Results and discussion As it would take too much space to present all the tests performed, the results of a sensitivity analysis are presented for the main parameters and components that were included in SIAR. In the following sections, the sensitivity analysis of SIAR is presented as the answer to eight questions. Each time, the final version of SIAR (S1) is compared to alternative versions. The results for the entire dataset are systematically presented and, where appropriate, an example extracted from the dataset is also shown.
However, it should be noted that only seven out of the 17 catchments in the dataset contain glacierized areas. Depending on the catchment, these glacierized areas account for from 1 to 14% of the total catchment surface area (see Table  1 ). Consequently, the influence of this process is not visible in an analysis of the results based on the entire dataset. Nevertheless, this process is not negligible in the most glacierized catchments both for runoff simulation and to avoid the risk of generating snow towers. As an example, Fig. 6  compares snowpack, icepack, FSC and runoff simulations with SIAR (S1) and an alternative version without the glacier component (S3a in Table  4 ) in the Avérole catchment at Bessans. This catchment is located between 2086 and 3581 m a. s. l. in the Alps, and has a surface area of 45 km 2 (14% of which is composed of glaciers, see Table  1 ).
Q3: Should a sublimation loss term be included in the model? Due to the relatively large amount of energy required for sublimation, related snow mass change rates are generally assumed to be small compared that required to melting  (Strasser et al. , 2008). However, sublimation may represent the only mass loss for cold snow. Fig. 5  suggests that an equivalent model fit is achieved on the entire dataset whether a sublimation term for ablation is considered (S1) or not (S3b). In fact, the simulated sublimated volumes are low with values ranging from 3 to 9 mm/year (Table  5 ), i. e. ~ 1% of annual snowfall on average at the catchment scale. This explains why including a sublimation term or not has little impact on water balance and leads to similar performance distributions. It should be noted that no sublimation was considered for temperatures below -5 • C since sublimation was indexed to PE which is zero below this threshold (see Eq.
The results on the entire dataset (Fig. 5 ) reveal that SIAR (S1) performed better only in FSC simulations compared to the version without the snowpack cold-content process (S3d): in particular, the mean and median values in NSE FSC are 0. 03 higher with SIAR. This improvement in performance led us to keep the snowpack cold-content process in the SIAR final structure. However, the associated weighting coefficient parameter (θ) was set to a fixed value of 0. 75, as its calibration resulted in only marginal improvement in evaluation over independent periods. Q6: Should the melt factor be constant or vary in space and over time? A variable melt factor was used based on the Ks parameter indexed on potential radiation to account for the variation of the melt factor in space and over time. There are indeed physical reasons (e.
g. less hours of sunshine in winter, lower snow surface albedo in spring) for recommending a lower melt factor in winter and a higher value in spring in the Northern Hemisphere. In addition, topography further accentuates the disparity in the snow cover. Thus, slope exposure influences changes in the snowpack: snow disappears faster on south-and west-facing slopes than on slopes exposed to north and east, solar radiation being weaker and of shorter duration on the latter. The results for the entire dataset (Fig. 5 ) show that SIAR (S1) reproduced local snow and runoff observations better than a version (S3e) based only on a constant degree-day-factor (Kf). The mean and median values in NSE SWE , NSE Q , and NSE 1/Q are between 0. 02 and 0. 05 higher with SIAR (S1). Therefore, the use of a variable part (Ks) indexed on potential radiation in the melt factor is clearly appropriate.