dataesr/scientific-paragraphs-categorization huggingface dataset:
Although polymorphisms at codon 211 had no effect on test outcome, relative test performance was different in animals homozygous or heterozygous for M codon 142 compared to the wild type (II), but this effect was not absolute, or statistically significant. The presence of M at codon 142 does appear to compromise to some extent the sensitivity of the Bio-Rad tests. The only absolute effect on test performance was seen in the samples from the goats of the experimentally challenged Cypriot panel that were polymorphic for serine or aspartic acid at codon 146, in which the performance of the Bio-Rad tests was profoundly affected. Aspartic acid, in particular, prevented the binding of at least one test antibody and resulted in false-negative results for samples from animals of this genotype. When these samples were further analyzed by western blot (data not shown; Chaplin, Papasavva-Stylianou, Simmons, Georgiadou, pers.
We have precisely described the experimental procedure for producing MsrP in order to avoid the common mistake, which would consist in using the E. coli BL21 (DE3) strain. This protocol was not sufficiently highlighted in our previous publication  [9] , leading to unsuccessful purification of such enzymes in other laboratories. StMsrP was purified, characterized and found to reduce free and protein-bound Met-O. The catalytic activities measured are similar to Fig. 3. StMsrP reduces free Met-O. A) StMsrP activities with Met-O and DMSO. The StMsrP activities were measured using benzyl viologen (BV) as an artificial electron donor under anaerobic condition. BV was initially reduced with sodium dithionite and oxidation was monitored at 600 nm after addition of the enzyme. Data presented are the average of three individual experiments ± S.
Acknowledgements We thank all the members of the  Ezraty group  for comments on the manuscript, advice and discussion throughout the work. We thank  Nassos Typas  ( EMBL-Heidelberg ) and  Tracy Palmer  ( Newcastle University ) for providing strains, and  Rebecca Stevens  ( GAFL, INRAE Avignon ) for critical reading of the manuscript. This work was supported by the  Fondation pour la Recherche Médicale , grant number  ECO201806006837 , to  Andrieu Camille. This work was supported by grants from the  Agence Nationale de la Recherche (ANR)  (# ANR-16-CE11-0012-02 METOXIC ) and the  Centre National de la Recherche Scientifique (CNRS)  (# PICS-PROTOX ). We also thank  J. F. Collet ,  L. Tarrago ,  Y. Cherrak ,  A. Battesti  and the  Py group  for helpful suggestions and comments on this work.
Re-analysis of past or modern datasets have also demonstrated that SAM children with low WHZ and SAM children with low MUAC have a similar risk of dying  [3, 20, 21]. These studies also revealed that both deficits have additive effects on mortality risk: SAM children combining a low MUAC and a low WHZ, as well as those combining oedema and low WHZ, have a significantly higher risk of death. Although MUAC measurements are logistically easier, especially in community screenings, the need to use both WHZ and MUAC criteria independently for identification of SAM and MAM and admission to treatment is explicitly mentioned in the international normative guidance  [8]. However, the use of MUAC as the only criterion for case finding and admission to therapeutic feeding programs has been increasingly promoted and applied in recent years  [22] [23] [24].
Methods Data for these analyses were obtained from Action Contre la Faim (ACF) International, an international humanitarian non-governmental organization that conducts multiple field nutrition surveys in humanitarian settings worldwide  [27]. Surveys conducted during 2007-2018 that measured both sex, age, height, weight, oedema and mid-upper arm circumference (MUAC) in children aged 6-59 months were included. All surveys included were population representative cross-sectional two-stage cluster surveys following standard survey and sampling procedure and usually conducted at the district level  [28]. Survey countries were grouped into six geographic categories: Latin America and the Caribbean; East and South Africa; Democratic Republic of Congo (DRC); West and Central Africa; East Asia and Pacific; and South Asia  [29].
DRC was kept as its own category due to the large number of surveys from the country. Countries that had fewer than five surveys conducted during 2007-2018 were excluded from the analyses. We considered that fewer surveys would have too few cases of acute malnutrition to produce reliable per-country estimates. The Middle East and North Africa regions were not included since none of the countries had five or more surveys conducted during the study period. Weight-for-height Z scores (WHZ) were calculated for all children using the WHO SAS macro, which applies the WHO 2006 growth standards  [30]. Children with missing data for age, sex, weight, height or MUAC and with age out of range (6. 0-59. 99 months) were excluded. Children were also excluded following WHO flagging criteria if they had WHZ that fell outside of +/-5 Zscores.
The addition of DHA maintains the integrity of the plasma membrane (Figure  1B ). Mitochondrial activity (to assess cell proliferation and/or viability) was measured with the MTT test at 24 and 48 h of treatment with VLCFA (10 and 20 µM) with or without DHA (50 µM) (Figure  1C ). The results were expressed as a percentage of the control value. When VLCFAs are used alone, mitochondrial activity falls relative to control (Figure  1C ). When DHA is used in co-treatment with VLCFA, mitochondrial activity is lower than the control but higher than with VLCFA alone. Mitochondrial activity is restored using DHA, indicating that there are more cells with functional mitochondria; this corroborates the data observed by phase contrast microscopy. The integrity of the plasma membrane was assessed using propidium iodide which is interspersed in the DNA only if the plasma membrane is permeable.
1% Tween 20, pH 8), the membrane was incubated overnight with the primary antibody (TBST with 1-5% milk). The antibodies directed against LC3-I/II (L8918) were from Cell Signaling (Ozyme, Saint Quentin Yvelines, France), and used at 1/1000 final concentration. An antibody directed against β-actin (Sigma-Aldrich) was used at 1/10,000 final concentration. After three washes with TBST, membrane was incubated (1 h, room temperature) with horseradish peroxidase-conjugated goat anti-rabbit antibody (Cell Signaling) diluted at 1/5000. The membrane was washed with TBST and revealed using an enhanced chemiluminescence detection kit (Supersignal West Femto Maximum Sensitivity Substrate, Thermo-Scientific) and Chemidoc XRS+ (Bio-Rad, Marnes la Coquette, France). The ratio LC3-II/LC3-I was calculated with Image Lab software (Bio-Rad).
A B S T R A C T Background: In patients with severe COVID-19, no data are available on the longitudinal evolution of biochemical abnormalities and their ability to predict disease outcomes. Methods: Using a retrospective, longitudinal cohort study design on consecutive patients with severe COVID-19, we used an extensive biochemical dataset of serial data and time-series design to estimate the occurrence of organ dysfunction and the severity of the inflammatory reaction and their association with acute respiratory failure (ARF) and death. Findings: On the 162 studied patients, 1151 biochemical explorations were carried out for up to 59 biochemical markers, totaling 15,260 biochemical values. The spectrum of biochemical abnormalities and their kinetics were consistent with a multi-organ involvement, including lung, kidney, heart, liver, muscle, and pancreas, along with a severe inflammatory syndrome.
Furthermore, even if some studies have evaluated the risk of COVID-19 related complications using a small set of biological markers at baseline, no study has evaluated, on a wide range of biochemical markers, the spectrum and the magnitude of biochemical disturbances, as well as their kinetics over time and their potential association with disease outcomes in patients with severe COVID-19. Using a big-data approach and multilevel modeling adapted for repeated measures on a well-phenotyped retrospective cohort of consecutive patients with newly diagnosed severe COVID-19, we were able to assess the occurrence of organ dysfunction (kidney, lung, heart, liver, muscle) and the severity of the inflammatory reaction as evaluated by biomarker kinetics in hospitalized patients. We assessed the association of the more representative biochemical markers with the risk of occurrence of ARF and death.
Description of the Nancy biochemical database As previously reported  [14] , the "Nancy Biochemical Database" is a prospectively maintained electronic database that increments the biochemical results of consecutive patients hospitalized in 67 healthcare departments at the University Hospital of Nancy (Supplementary Fig. 1 ). The biochemical data are connected to clinical data at the patient level through the electronic health record system of the University Hospital of Nancy. All biochemical data that were obtained in patients with COVID-19 were extracted for the purposes of the study using the GLIMS laboratory information management system v9 (MIPS France S. a. r. l. , Paris, France). Clinical data were retrieved through electronic chart review using DxCare Ò software (Dedalus France, Le Plessis Robinson, France).
Evidence before this study We performed a literature search to assess the available data regarding the follow-up of multi-organ dysfunction and inflammatory status using biochemical biomarkers kinetics in patients with severe COVID-19. We searched PubMed from inception to April 4, 2020, for studies that have reported clinical and biological data, and disease outcomes in patients with COVID-19. In patients with severe COVID-19, no data are available on the follow-up of multi-organ dysfunction and inflammatory status by assessing the kinetics of biochemical biomarkers and its association with disease-related complications.
Added value of this study In this retrospective cohort study on 162 consecutive patients with newly diagnosed severe COVID-19, we report the first longitudinal analysis using an extensive biochemical dataset of serial data (59 biochemical markers; 15,260 biochemical measures) and time-series design to estimate the occurrence of organ dysfunction and the severity of the inflammatory reaction and their association with acute respiratory failure (ARF) and death. Among all the studied biomarkers, only urea nitrogen and C-reactive protein (CRP) were independently associated with acute respiratory failure.
Study population, database structure, and main biochemical abnormalities observed in severe COVID-19 Between March 1, 2020, and March 25, 2020, 162 patients were admitted to one of the healthcare departments of the University Hospital of Nancy for severe COVID-19 (Supplementary Table  1 ). The median age was 66 (IQR, 56 to 77) years, and the proportion of males was 59% (Table  1 ). No patient received hydroxychloroquine or remdesivir. During the study period, 1151 biochemical explorations were carried out for up to 59 biochemical markers (46 in the blood and 13 in the urine), totaling 15,260 biochemical values. The distribution of the 59 biochemical markers is reported in Table  2  (blood) and Supplementary Table  2  (urine). The kinetics over time of the biochemical abnormalities is reported in Figs.
A set of 20 biochemical markers with at least 250 iterations was systematically screened for the association with the occurrence of ARF using ROC analysis. All markers with a statistically significant ROC-defined threshold were assessed using time series analysis to calculate the percentage of time below or above the defined threshold. The calculated summary effects are reported as percentages of the total time of observation with the 95% CI, as noted in the Forest plot. CoV-2 on pericytes may result in endothelial cell and microvascular dysfunction  [36]. A retrospective single-center case series of 187 patients with COVID-19 from China showed that myocardial injury was significantly associated with the risk of death  [37]. To date, descriptive data on COVID-19 did not report obvious cholestasis in patients with severe forms  [38, 39].
We acknowledge several potential limitations of the study that should be considered in the interpretation of our findings. Although we have studied an extensive follow-up dataset, our study is based on a relatively small number of patients, and our results need to be confirmed in independent studies. Nevertheless, we have systematically included all consecutive patients with newly diagnosed severe COVID-19, which allowed us to describe the biochemical abnormalities on a homogeneous population. The study design is retrospective; nevertheless, we used data from a structured and prospectively maintained database  [14]. In our study, the follow-up of patients was relatively short, which could lead to an underestimation of the risk of COVID-19 related mortality. Patients with severe COVID-19 can exhibit immunological determinants of a cytokine storm  [42] [43] [44] [45] , notably with high levels of CXCL10, CCL7, and IL-1 receptor antagonist, and which could influence the risk of lung injury and fatal outcome  [45].
In conclusion, in this retrospective, longitudinal cohort study, using an extensive biochemical dataset of serial data and time-series design on consecutive patients with newly diagnosed severe COVID-19, the follow-up of biochemical biomarkers kinetics was consistent with a severe multi-organ involvement along with a severe acute inflammatory response. High levels of CRP and urea nitrogen were potential predictors of ARF among patients with severe COVID-19. A set of 20 biochemical markers with at least 250 iterations was systematically screened for the association with the occurrence of in-hospital mortality using ROC analysis. All markers with a statistically significant ROC-defined threshold were assessed using time series analysis to calculate the percentage of time below or above the defined threshold.
Data sharing statement Anonymized patient data are available for use in collaborative studies to researchers upon reasonable request (abderrahim. oussalah@univ-lorraine. fr). Data will be provided following the review and approval of a research proposal (including a statistical analysis plan) and completion of a data-sharing agreement. Responses to the request for the raw data will be judged by the IRB of the University Hospital of Nancy. diagnostic assessment. Zakia Aitdjafer (MD), University Hospital of Nancy, for laboratory diagnostic assessment. V eronique Venard (PharmD, PhD), University Hospital of Nancy, for laboratory diagnostic assessment. Alain Lozniewski (PharmD, PhD), University Hospital of Nancy, for laboratory diagnostic assessment.
Acknowledgments We would like to warmly thank all the medical ( Dr. Patricia Frank ,  Dr. Anne Debourgogne ) and the technical staff of the  Laboratory of Biology and Biopathology of the University Hospital of Nancy  for their valuable contribution to the present work. Additionally, we would like to acknowledge the following collaborators. All collaborators approved the final draft. Matthieu Garcia  (MSc),  University Hospital of Nancy , for data management on the  "Nancy Biochemical Database". Isabelle Chouviac (PharmD), University Hospital of Nancy , for quality support on laboratory diagnostic assessment. Sibel Berger  (PhD),  University Hospital of Nancy , for quality support on laboratory diagnostic assessment. Audrey Jacquot (MD),  University Hospital of Nancy , for management for patients.
Study design The DIACART (for "DIAbète et Calcification ARTerielle") study is a prospective monocentric cohort study  [4]. The recruitment period extended from February to October 2014. Inclusion criteria were: Type 2 Diabetes with at least a history of coronary artery disease and/or peripheral arterial occlusive disease and/or age > 50 years for men and > 60 years for women. Exclusion criteria were:  [1]  an estimated glomerular filtration rate (GFR) calculated by the modification of diet in renal disease equation < 30 ml/min,  [2]  a history of lower limbs angioplasty and/or bypass, or  [3]  type 1 diabetes. The study was approved by the local ethics committee and registered in ClinicalTrials. gov (Identifier: NCT02431234). All patients were informed of the study objectives and procedures.
Funding DIACART study was funded by the  French Ministry of Health  grant (PHRC Code projet:  P111105 ;  N° ID RCB: 2013-A00896-39 ). This study was supported by a fund from the  Lilly Company. The company was neither involved in the design of the study nor in data collection. The research activities of C. E. Aubert were supported by a doctoral research scholarship from the  University of Lausanne. The authors have no relevant conflicts of interest to disclose.
Data collection Data were collected between September 2011 and August 2012. Trained nutritionists administered the survey through face-to-face interviews with the mothers. The survey inquired about sociodemographic characteristics of the household, current and future family planning practices of the mother, access to maternal and child health services, and mother's knowledge and practices related to infant feeding. Questions on infant feeding practices focused on the duration of total and exclusive breastfeeding, which was assessed using the life-long approach  [15] , the age of introduction of formula milk and solid food, and the reasons for breastfeeding, not breastfeeding, and breastfeeding cessation. Anthropometric measurements of the mother and child were collected. This study was approved by the Institutional Review Boards at the American University of Beirut (NUT.
Results Of the 1194 eligible mother-child dyads that were contacted, 1029 participated in the survey (response rate 86%). Twenty-four surveys with missing data were excluded, leaving 1005 mother-child dyads in the analysis. At the time of the interview, 947 children were 40 days or older and had complete information about feeding practices at 40 days, while 893 children were 6 months or older and had complete information about feeding practices at 6 months and thereby included in the analysis at 40 days and at 6 months, respectively. Prevalence of exclusive breastfeeding and other infant feeding practices at 40 days and at 6 months The prevalence of infant feeding practices at 40 days and at 6 months are presented in Fig. Sociodemographic, maternal, and child characteristics by infant feeding practice at 40 days and at 6 months Sociodemographic, maternal, and child characteristics by infant feeding practice at 40 days and at 6 months are presented in Table  1.
, 2014). The novelty of the present study relies on the fact that complex and realistic foods that differed only in structure, texture and water content were studied, and that the kinetics of macronutrient hydrolysis (proteins, carbohydrates and lipids) as well as micronutrient release from the matrix were recorded. For this purpose, four micronutrient-enriched food matrices were designed, with identical dry matter composition. They were enriched with a lipophilic (lutein) and a hydrophilic (vitamin B9) micronutrient in order to study the impact of their solubility on their release. Moreover, Principal Component Analysis and Multiple Factor Analysis were performed to deal with the complexity of the dataset obtained and were consequently a useful tool to study kinetics phenomena  (Lechevalier et al.
Preparation of foods Four foods enriched with vitamin B9 and lutein were produced according to the flowcharts and composition presented in Figure  1. The kneading, whisking and mixing steps as well as the heat treatment for Custard were carried out with a Thermomix® TM5 kitchen robot (Vorwerk, Wuppertal, Germany). Biscuit, Sponge cake and Pudding (solid foods) were cooked using a semi-professional convection oven (De Dietrich, Niederbronn-Les-Bains, France). The dry matter in the food consisted of 17% proteins, 52% carbohydrates and 30% lipids. Food lipid content was controlled by lipid weighing after Folch extraction, and protein content by the Kjeldahl method (data not shown). Custard (liquid food) was prepared as a packed powder to be rehydrated and heated at the time of use. Pudding, Sponge cake, and Biscuit were stored at -20°C until use and then defrosted at room temperature.
Aqueous phases and any gas bubbles in the slices may appear as black holes in the micrographs. There were at least two samples of each product, and at least 16 micrographs were recorded for each sample. The micrographs presented in Figure  3  were considered to be representative of the different product samples. Confocal micrographs of the Red Nile-stained lipid droplets of the different matrices (channel 2) were converted to binary images using the "Otsu" thresholding algorithm of Fiji-win64 software (Image J 1. 51n, National Institutes of Health, USA). The segmentation procedure was validated by visual comparison of the resulting binary image with its original image. The particle areas and perimeters were measured using the Analyze Particles measurement tool (in µm²), and the data generated were analysed according to  Bornhorst, Kostlan, & Singh (2013).
Quantification of lipid classes The lipid classes (extracted from digesta) were separated on silica thin layer chromatography (TLC) plates using hexane:diethyl ether:acetic acid (70:30:1, v:v) as the eluent. The lipid bands were visualised by spraying the plates with a 10% (w/v) cupric sulphate solution in 8% (w/v) orthophosphoric acid and heating for 10 min at 150°C. After scanning using a Labscan (GEHealthcare, ImageScanner III), the band density was estimated using Image J software. Six classes were separated: Triglycerides (TAG), Free Fatty Acids (FFA), Cholesterol (CHOL), Diglycerides (DAG), Monoglycerides (MAG) and Phospholipids (PL). Lipolysis could therefore be estimated prior to the ultimate stage of FFA.
Principal component analysis A Principal Component Analysis (PCA) was performed on the whole dataset to highlight the correlations between the variables and to characterise the evolution of the four foods during digestion in view of these variables. PCA and all the following statistical analyses were performed using the FactoMineR package of R software 3. 5. 2 (Lê, Josse, & Husson, 2008; R Development Core team, 2007). "Digestion time" was added as a supplementary variable. The variables were automatically standardised (mean centred and scaled) by the software to give them all the same importance.
The gastric phase is primarily a macronutrient solubilisation step, whereas the intestinal phase is a hydrolysis step In order to highlight correlations between the food matrix, digestion time, and all the characteristics measured on the digesta, a principal component analysis (PCA) was carried out (Figure  8A ). The first two principal components (PCs) account for a cumulative variability of 72%. A significant portion of the information provided by the dataset could thus be plotted in 2D defined by PC1 and PC2. PC1 (60. 74% of the variability) is positively correlated with markers of food disintegration (total lipids and peptides released in the soluble fraction), but also with markers of lutein release and of macronutrient hydrolysis: lipolysis (FFA, MAG, DAG measured by TLC and lipolysis degree measured by GC), amylolysis (oligosaccharides released), proteolysis (free amino groups and all classes of peptides < 25 kDa); PC1 is negatively correlated with triglycerides.