dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements The authors benefitted from the  French GDR "Aquatic Ecotoxicology " framework which aims at fostering  stimulating scientific discussions and collaborations for integrative approaches. This work has been supported by the  APPROve  project ( ANR-18-CE34-0013-01 ) and  Dyn-Microbiome  ( ANR-20-CE34-0012 ) project funded by the  ANR (Agence Nationale de la Recherche). Carnot Eau & Environnement Institute  and the  INRAE AQUA Department (PhD fellowship Auréline Lalouette ) have also supported this work.
The primary outcome was brain maturation assessed by diffusion tensor imaging (DTI) using Tract-Based Spatial Statistics (TBSS) analysis. Results: We included 120 infants (60 per group) in the trial; mean (range) gestational age was 26 þ3 (22 þ6 -28 þ6 ) weeks and postmenstrual age at scan was 41 þ3 (39 þ1 -47þ 0 ) weeks. Ninety-two infants underwent MRI imaging, and of these, 90 had successful T1/T2 weighted MR images and 74 had DTI data of acceptable quality. TBSS did not show significant differences in mean or axial diffusivity between the groups, but demonstrated significantly higher fractional anisotropy in several large WM tracts in the ARA:DHA group, including corpus callosum, the anterior and posterior limb of the internal capsula, inferior occipitofrontal fasciculus, uncinate fasciculus, and the inferior longitudinal fasciculus.
Because diffusivity and tissue anisotropy changes during brain maturation, these DTI metrics provide indirect measures of axonal development, cell density and myelination  [20e23]. Tract-based spatial statistics (TBSS) is an observer-independent tool for analysing DTI-data  [24]. Emerging evidence suggest that TBSS can capture early changes in FA that significantly correlate with motor and cognitive outcomes in early childhood  [2,25e29]. Based on the assumption that ARA and DHA serve as necessary components of neurocellular structures and inhibitors of harmful inflammatory processes, the primary aim of the ImNuT (Immature, Nutrition Therapy) trial was to test the hypothesis that early enteral supplementation with these fatty acids improves WM microstructure assessed by DTI at term equivalent age (TEA).
Study design The present double-blind RCT (NCT03555019) was conducted at Oslo University Hospital (OUH) in Norway. The study protocol has previously been published  [30]. The study was approved by the Regional Committee for medical and health Research Ethics, REC South East (2017/1067). A data monitoring committee with access to reported adverse effects and clinical outcomes reviewed the study for safety and efficacy, and we followed the Consolidated Standards of Reporting Trials (CONSORT) guideline.
MRI acquistion Brain examinations were performed using a 3-T Siemens Magnetom Prisma MRI scanner with a dedicated 32-channel head coil (Siemens Healthineers, Erlangen, Germany). The brain MRI protocol consisted of T2-weighted sequences (TR 4900 ms, TE ¼ 153 ms, FOV 160 mm Â 125 mm, pixel ¼ 0. 8 mm Â 0. 8 mm with interpolation 0. 4 mm Â 0. 4 m, 114 slices, slice thickness 1 mm, flip angle 142 ) and T1-weighted 3D MP2RAGE sequences (TR 6000 ms, TI1 ¼ 1100 ms, TI2 ¼ 2800 ms, slice thickness 0. 8 mm, FOV 180 mm Â 135 mm, voxel size ¼ 0. 8 Â 0. 8 Â 0. 8 mm 3 , FA1 ¼ FA2 ¼ 4 ). The diffusion weighted imaging (DWI) data were acquired using a single-shot spin-echo multiband sequence with a monopolar gradient scheme  [33]. The diffusion protocol allowed for whole brain coverage with an isotropic resolution of 1.
MRI data processing and harmonization A pediatric neuroradiologist inspected the images before clinical results were provided to responsible medical staff and caregivers. For study purposes, brain morphology was scored according to the Kidokoro scoring system  [34]. The global brain abnormality score was calculated based on regional scores obtained from cerebral WM, cortical gray matter, deep gray matter and cerebellum, and brain injury was classified into no, mild, moderate or severe. Evaluations were based on T1-and T2-weighted scans and performed by one of two neuro-radiologists experienced in neonatal imaging. In cases of discrepancy, images were re-evaluated and consensus was achieved from both evaluators. For the TBSS, anonymized raw MRI data were uploaded to the Services for Sensitive Data (TSD, University of Oslo, Oslo) for processing and analyses.
DTI images were visually inspected and evaluated for general artefacts. Images eligible for further analysis were processed using an adapted optimized pipeline and tools provided by the Oxford Centre for Functional Magnetic Resonance Imaging of the Brain Software Library (FSL), version 6. 0. 5 (https://fsl. fmrib. ox. ac. uk) and Diffusion Imaging in Python (Dipy) [35e37]. Image quality was improved using denoising and removal of Gibbs-ringing artefacts  [38, 39]. FSL was used to remove the skull and to correct for susceptibility induced distortion, eddy-currents and motion artefacts. In the present study, we used a population specific diffusion template to avoid bias related to image alignments to a standard MNI (Montreal Neurological Institute) adult template. This template was based on images from five infants of similar gestational ages and with a Kidokoro score of 0 (normal) and it was created using the DTI ToolKit  [40].
Clinical assessment, nutrient intakes and growth Neonatal morbidities were diagnosed according to standard definitions and included severe brain injury defined as the presence of intraventricular haemorrhage grade !3 or cystic periventricular leukomalacia, retinopathy of prematurity, necrotizing enterocolitis ! stage 2A, bronchopulmonary dysplasia, patent ductus arteriosus requiring treatment, and late-onset septicaemia  [31]. We used the Nutrium™ software package (www. nutrium. se, Nutrium AB, Umeå, Sweden) for calculation of daily nutrient intakes during hospitalization, including ARA and DHA intakes from all other nutrient sources (human milk, fortifier, formula and parenteral nutrition). The reference values for ARA and DHA in human milk was 16 and 8 mg/dL, respectively, and was not different between maternal and bank milk  [42].
Statistical analyses The sample size was based on data from a previous nutritional intervention study, the PRENU-study  [44]. In that study, the mean (SD) MD in the superior longitudinal fasciculi was 1. 18 Â 10 À3 (6. 18 Â 10 À5 ) mm 2 /s in the intervention group and 1. 24 Â 10 À3 mm 2 /s (5. 70 Â 10 À5 ) in the control group. Based on these SDs, we calculated that 36 infants in each group were required to achieve 80 % power to detect a 0. 04 Â 10 À3 mm 2 /s difference in mean MD, with a two-sided significance level of 5 % (estimated sample size for two-sample comparison of means). Assuming 40 % loss to follow-up (20 % mortality/drop-outs, 20 % non-interpretable images), the estimated total study sample was 120 infants. We performed efficacy analyses on the intention-totreat (ITT) population.
To compare differences between groups we used independent samples t test for continuous variables and the chi-square test or Fisher's exact test for categorical variables. For variables not normally distributed, ManneWhitney U test was used. For the outcome analyses, we also used a linear mixed-effects model with maximum-likelihood estimation to adjust for possible correlations related to 1) multiple births (random intercept) and 2)  the stratified randomization based on the balancing variable smallfor-gestational-age status (fixed effect)  [45, 46]. Analyses were performed using IBM SPSS Statistics version 26 and STATA statistical software. Voxel-based statistical analyses of DTI data were performed using TBSS. All full data sets with b-values of 0 and 1000 with good quality were used to calculate the DTI metrics.
A general linear model was used to adjust for sex, gestational age at birth, small-for-gestational-age status, multiple births and postmenstrual age at MRI acquisition. Following family-wise error rate correction and Threshold-Free Cluster Enhancement  [40] , which was applied for n ¼ 5000 permutations, significance was set as p < 0. 05. Results were visualized as a color-coded overlay on the mean FA skeleton. Since early onset septicaemia and postnatal protein intake were unevenly distributed between the groups, and both may affect WM integrity, they were added to the model post-hoc. In the protein adjusted analysis images from 2 infants were excluded; one extreme outlier (protein intake 5. 25 g/kg/d; ARA:DHA group) and one infant with missing nutrient intake data (control group).
Acknowledgements We would like to thank the Data Monitoring Committee members:  Inge Christoffer Olsen ,  Ketil Stordal  and  Trond Markestad ;  Cathrine Nygaard Espeland  for controlling data and helping with figures; pharmacists  Anette Ronaes Sollie  and  Rebwar Saleh  for preparing the fatty acids; radiographers Åsmund  Hagen-Kersten  and  Ane Julie Krog  for acquiring MR images; the neonatal nurses and doctors at  Oslo University Hospital  for their support; and not at least the participating families for their commitment to the study.
2. 5. Statistical analysis Results were expressed as mean ± SD. Zn-MTs analysis was repeated in four replicates for each of two independent samples in group, resulting in n = 8 for each group. For all other traits, eight individuals were analyzed in each group separately. Data were tested for normality and homogeneity of variance by using Shapiro-Wilk test and Levene's tests, respectively. Whenever possible, data were normalized by Box-Cox common transforming method. One-way ANOVA was used to test the effect of treatments, followed by post hoc procedures. For the data that were not normally distributed, non-parametric tests (Kruskal-Wallis ANOVA and Mann-Whitney U test) were performed. Normalized, Box-Cox transformed data were subjected to the principal component analysis (PCA) to assess the relations between measured parameters utilizing rotation method Varimax with Kaiser Normalization, and Canonical discriminant analysis was utilized for the separation of the exposed groups.
Data integration Canonical Discriminant Analysis was applied to the datasets to identify relations between indices (Table  S4 ). Fig. 9a  illustrates the score plot (component 1 vs. component 2) and shows a good distribution of the variables, with two clearly opposite groups explaining 59. 4 % of the total variance of the dataset. The first principal component (PC1) explained 41. 4 % of the total variance. It included all oxidative stress indexes, among them PC, Cu,Zn-SOD, catalase, PhO, and GSH, and also biotransformation enzymes EROD and GST in the positive part of the axis and TCA in opposite position with the probability of more than | 0. 5|. The second principal component explained 18. 0 % of the total variance with the location in the positive part of axis proteases caspase-3, CtD total and extralysosomal, and also ChE and PhO total and with laccase inhibitor.
Acknowledgments This work has been granted to  Melissa Palos-Ladeiro  and  Oksana Stoliar  by the research program  PHC DNIPRO  n  46800RK  and  Ministry of Education and Science of Ukraine  (##  M-70/2021 ,  M-13/2022 ) under the  French-Ukrainian Cooperation Program. Authors are grateful to  Mr. Ivan Shevchenko  for his assistance in the sampling in the Kherson area.
That variability was partly explained by individual patients' characteristics such as patient body size, age (in pediatric patients), genetic  [6] , and more recently metabolomic markers  [7]. GSTA1 promoter polymorphisms, coding for glutathione-s-transferase A1 primarily involved in Bu metabolism, have been identified as factors contributing to the IIV in Bu PK and outcomes, both in adults [8À14] and pediatric patients  [15À18]. In a multicenter randomized BuCyBu study (NCT01779882) conducted in adult patients undergoing HSCT for hematological malignancies in Switzerland, patients were randomized to receive cyclophosphamide either before or after Bu administration  [19]. The comparison of noncompartmental analysis results suggested a higher exposure, in terms of firstdose AUC, in GSTA1*B haplotype carriers  [8].
Patient Population Adult patients (n = 60) from the study BuCyBu (NCT01779882)  [4] , for whom clinical, demographic, and PK data were available, were included in the present analysis. These patients consented for their biological samples to be stored in the Swiss Transplant Cohort Study (SCTS) biobank and to be used for research purposes. DNA samples extracted from whole blood specimens were received upon approval (Application ID: FUP 151) by the SCTS Biobank scientific committee. The patients' DNA samples were genotyped for GSTA1 promoter variants as previously described  [20]. The clinical and demographic characteristics of the study cohort are described in Supplementary Material S1.
Population Pharmacokinetic Analysis The PopPK model was developed in Phoenix NLME software (Certara, version 8. 2) with the first-order conditional estimation and extended least squares (FOCE-ELS) method. The one and two-compartment models were explored as structural models, and the additive, proportional and mixed residual errors were tested for the statistical model. An exponential IIV was assumed for the PK parameters (log-normal distribution). Due to the availability of concentration data only from the first dose, interoccasion variability of the PK parameters was not estimated. The body weight of the patients was incorporated into the model using theoretical allometric scaling equations. Other derived body size metrics were not included in the model building process due to the absence of height data for 12 patients.
A stepwise covariate screening was conducted using the Bayesian information criterion (BIC) as the objective function value, considering covariates in addition to body weight. The covariates tested on top of body weight were preconditioning levels of Aspartate Aminotransferase (ASAT), Alanine Aminotransferase (ALAT), Bilirubin, Albumin, the age of the patient, GSTA1 genetics and the randomization group (Cy before Bu versus Bu before Cy). The model qualification included the assessment of goodness-of-fit plots, bootstrap re-estimation on 500 resampled datasets, and prediction-corrected visual predictive check plots with 1000 simulated replicates. These steps were undertaken to ensure the reliability and robustness of the developed model  [23].
PopPK Model-Based Simulations Considering the covariates of the 48 patients in our dataset for whom the height was available, we conducted Monte-Carlo simulations to generate 50 replicates for each patient. These simulations evaluated a range of single doses varying from 0. 65 mg/kg to 1 mg/kg of ABW, ideal body weight (IBW), or adjusted ideal body weight at 25% fat (AIBW25)  [24] , as well as doses ranging from 25 to 39 mg/m 2 of body surface area (BSA) calculated with the Mosteller formula  [25]. The adjusted body size metrics were calculated using the equations 1 to 4. Males: IBW kg ð Þ ¼ 50 þ 0:91 Â height cm ð ÞÀ152 ð Þ ð1Þ Females: IBW kg ð Þ ¼ 45 þ 0:91 Â height cm ð ÞÀ152 ð Þ ð2Þ AIBW25 kg ð Þ ¼ IBW kg ð Þþ 0:25 Â ABW kg ð Þ À IBW kg ð Þ ð Þ ð3Þ BSA m 2 À Á ¼ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ABW kg ð Þ Â height cm ð Þ 3600 r ð4Þ We also tested a dosing regimen based on ABW for individuals with BMI < 27 kg/m 2 and AIBW25 for BMI > 27 kg/m 2 , as suggested by Nguyen et al.
Evaluation of Limited Sampling Strategy and Model Implementation in Tucuxi TDM Software PFIM software 4. 0  [26]  with Fisher information matrix optimization using Federov-Wynn method was used for the determination of an optimal 2sample design for TDM. The provided input time points for the selection of the optimal sampling timepoints were 0 h to 0. 25 h to 0. 5 h to 1 h to 4 h from infusion end. Once the optimal sampling schedule chosen, we tested the accuracy of model predictions of a single dose AUC 0-1 with limited samples compared to the Maximum A Posteriori (MAP) estimations with the extensive sampling used in the study. The model was then implemented in Tucuxi software  [27]  using the online drug file editor (available from: http://drugeditor. tucuxi. ch/) and a proficiency test was performed to assess the concordance between single doses AUC 0-1 obtained with Tucuxi and limited sampling, compared to those obtained with Phoenix software and full sampling.
PBPK Model Development To evaluate the impact of obesity on Bu PK, a full-body PBPK model for Bu was developed using Simcyp software  [28]  (Certara, version 21). The construction of this model involved adapting a previously validated pediatric model (Ben Hassine, Khier et al. , submitted manuscript) to the adult population. Drug-related parameters were obtained from literature sources, predicted using Simcyp models, or estimated based on observed data from the study population. A detailed overview of the drug-related parameters can be found in Table  1  [29À34]. Considering that less than 2% of Bu is eliminated unchanged in urine  [6] , it was assumed that the entire elimination of Bu occurs through hepatic metabolism. The reported K M value for Bu conjugation with cytosolic enzymes responsible for its metabolization, as previously described by Bredschneider et al.
Single-dose simulations of 0. 8 mg/kg ABW infused over 2 hours, were conducted on 10 virtual cohorts, each comprising 10 patients (totaling 100 virtual patients for each population). The model simulations with the virtual cancer population were validated against observed data from our cohort, with a comparison of observed versus simulated concentrations, as well as the area under the concentration-time curve (AUC 0-1 ) determined using MAP, and the observed maximum concentration (C max ). A geometric mean fold error (GMFE), defined as the geometric mean of simulated parameters divided by the geometric mean of observed parameters, falling within the range of 0. 8 to 1. 25, was used as the criterion for PBPK model validation. Visual assessment of the concordance between observed and simulated concentrations was also performed.
PopPK Model Development and Validation The dataset included 296 measured Bu plasma concentrations, none of them being below the limit of quantification of the analytical methods. Four concentrations from 4 patients were missing. A two-compartment model with linear elimination, zero-order infusion rate and proportional residual error was the most adequate structural model to describe our PK data. The model was macro-parametrized in terms of systemic CL, intercompartmental clearance (Q), Volumes of distribution of the central compartment (V c ) and peripheral compartment (V p ). V p and its random variability could not be precisely estimated. V p was therefore estimated as a factor of V c. The theoretical allometry of bodyweight for CL, V c and Q improved the BIC by 29. 52 units compared to the structural model.
Limited Sampling Strategy Evaluation and Model Implementation in Tucuxi Because we showed in the previous part that TDM of Bu is necessary for the achievement of the target of 3. 7 to 5. 5 mg. h/L, we tested a limited sampling strategy to facilitate the implementation of routine TDM. A 2-sample TDM design was proposed by PFIM software, with samples taken at peak and trough concentrations (at the end and 4 hours after the end of infusion). The plots in Supplementary Material S4 show a very good concordance between the AUC 0-1 predicted with limited sampling in tucuxi software, compared to full sampling (Linear regression: R 2 = 0. 9772; Bland-Altman: 95% limits of agreement: -7. 98% to 6. 49%), confirming the suitability of Tucuxi software for PK parameter estimation with limited sets of samples.
PBPK Model Simulations One of the challenges in our study cohort was the scarcity of obesity in the dataset (only 1 patient with BMI >30 kg/m 2 , Supplementary Material S1) making it difficult to evaluate dosing strategies in obese patients. For this reason, we attempted to explore dosing regimens that are suitable for both obese and nonobese patients using relevant virtual populations in Simcyp software, while exploring physiological and PK differences between the different virtual populations. The model simulations in the cancer population showed an adequate concordance with simulated concentrations (Figure  3A ). Nonetheless, a higher variability around the peak concentration could not be entirely described by the model. The GMFE of AUC 0-1 was 0. 92 and the GMFE of C max was 1. 04, both results being within the acceptable range of 0.
Our study supports the relevance of TDM of MAC Bu at least for exposure target attainment. Whether a better targeting of the therapeutic window will be translated into improved outcomes remains to be further proved, despite the current evidence  [45]. As previously mentioned, the study had a limitation due to the small number of obese patients included. Given the growing prevalence of obesity in several countries, we recognized the importance of exploring this aspect, even in the absence of in vivo data. Therefore, we conducted an in-silico evaluation of Bu dosing for obesity using the physiological parameters of the virtual populations of the Simcyp software. It would be beneficial to prospectively evaluate the BSA-based dosing strategy coupled with TDM. Another limitation of the study was the lack of measurements of Bu concentrations in multiple dosing occasions, which hindered the analysis of inter-occasion variability.
ACKNOWLEDGEMENTS We thank  Dr. Simona Mlakar  and  Mme. Mary Boudal-Khoshbeen  for helping us with the genotyping of GSTA1 promoter SNPs. We also thank  Dr. Tiago Nava  for his help in organization of the pharmacogenomic part of the study for STCS submission. We express our gratitude for the help in DNA sample shipment and organization by  Mr. Denis Marino  from  CANSEARCH Research platform  on pediatric oncology and hematology and  Mme. Simona Rossi  from biobank of  STCS. We acknowledge the  CANSEARCH foundation  for the financial support. Financial disclosure: This work was financially supported by  Baxter SA  and  Robapharm/Pierre Fabre SA  and the  CANSEARCH foundation. Conflict of interest statement: None to declare related to the present work. Independently from this work: YC has received honoraria for participation in symposia and advisory boards from  MSD ,  Novartis ,  Incyte ,  BMS ,  Pfizer ,  Abbvie ,  Roche ,  Jazz ,  Gilead ,  Amgen ,  Astra-Zeneca ,  Servier ; Travel support from  MSD ,  Roche ,  Gilead ,  Amgen ,  Incyte ,  Abbvie ,  Janssen ,  Astra-Zeneca ,  Jazz ,  Sanofi , all via the institution.
Background. -Drug-induced hyperglycemia and diabetes have negative and potentially serious health consequences but can often be unnoticed. Methods. -We reviewed the literature searching Medline database for articles addressing druginduced hyperglycemia and diabetes up to January 31, 2023. We also selected drugs that could induce hyperglycemia or diabetes according official data from drug information databases Thériaque and Micromedex. For each selected drug or pharmacotherapeutic class, the mechanisms of action potentially involved were investigated. For drugs considered to be at risk of hyperglycemia or diabetes, disproportionality analyses were performed using data from the international pharmacovigilance database VigiBase. In order to detect new pharmacovigilance signals, additional disproportionality analyses were carried out for drug classes with more than 100 cases reported in VigiBase, but not found in the literature or official documents.