dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments Trial Registration numbers  NCT02243280 ,  NCT02243293 ,  NCT02604017 ,  NCT02640482 ,  NCT02640157 ,  NCT02636595 ,  NCT02642432 ,  NCT02651194 , and  NCT02446717. Medical writing support was provided by  Daniel O'Brien, PhD , of  AbbVie. AbbVie sponsored the studies; contributed to their design, collection, analyses, integrated analysis, and interpretation of the data; and participated in the writing, review, and approval of the manuscript.
Flow cytometry To confirm UC-MSC and rat BMSC characteristics, positive (CD73, CD105, CD44 and CD90) and negative (CD79α, HLA-DR, CD14, CD45, CD34) cell-surface markers were detected by flow cytometry. The cells (1 × 10 6 ) were harvested by trypsin digestion, washed with phosphate buffer saline (PBS), blocked with 2% FBS at 4°C for 30 min and then incubated individually with FITC/PE/ APC-conjugated antibodies (BD Biosciences, USA) at 4°C for 1 h. Unlabeled cells were used as a control for all antibodies. The cells were re-suspended in 0. 4 mL PBS and analyzed on a FACSCalibur (Becton Dickinson, USA). Data were processed using FlowJo software (Java Software).
Then, the cells or sections were incubated with Alexa Flour 488/546-conjugated donkey anti-rabbit/mouse secondary antibody (Invitrogen, USA) for immunofluorescence or HRP goat anti-rabbit/mouse secondary antibodies (KPL, USA) for immunochemistry. Diaminobenzidine tetrahydrochloride (ZSGB-Bio, China) was used for immunochemical staining. The negative control were treated with the same steps without primary antibodies incubation. The images were captured under a microscope (Olympus BX51, Japan). Quantitative analysis was conducted in a blinded fashion with Image-Pro Plus Software.
Discussion With the initiative of developing easy and convenient prophylactic techniques for OA patients, this study aimed to test the action of KGN on cartilage regeneration by oral administration and the further pharmacological mechanism. After confirming that oral KGN administration could attenuate OA injury, we wished to confirm whether KGN could actually disperse within the cartilage. While we detected KGN in the blood by HPLC/MS, to our surprise, new chromatographic peaks in the blood were detected (data not shown) and no KGN was found present in cartilage after oral administration of KGN. The finding raised the question as to what happens to KGN upon entering the blood stream. Previous studies have shown that amide bond cleavage can be catalyzed by various enzymes, such as serine proteases, metalloproteases, cysteine proteases and aldehyde oxidase  [35, 36].
3 °C) of the animal (Animal Ageing and Longevity Database, http://genomics. senescence. info/species/)  90  , but became more evident above 39. 5 °C. Variant (MACMU)NAT1_p. Leu89Phe appeared rather resilient to thermal stress, losing activity at similar rate as the reference protein, despite its demonstrated instability during expression/purification. Other variants were more sensitive to the effects of thermal stress (Fig. 5 ). To overcome the aforementioned limitations, we also tried differential scanning fluorimetry, a well-standardized method in our laboratory  61, 73, 74  , to assess thermal stability of (MACMU)NAT1 variants. However, with the exception of the reference protein, this approach was hampered by the insufficient purity of recombinant preparations. In the future, it may be useful to try alternative thermal denaturation assays, described in the literature  91.
b A 50% enzymatic activity threshold, relative to the (MACMU)NAT1_1 reference protein, was used to assess the effect of each polymorphism as either "moderate" or "substantial". The average value and corresponding range of relative (%) specific activities measured with PABA, 5AS and PANS are provided in parentheses. Variant (MACMU)NAT1_p. Glu155Gln provided a broad range of activities, depending on which substrate was used, and the overall effect was assessed as "marginal/moderate". c The predicted effect of each polymorphism on (MACMU)NAT1 protein structure/function was assessed by structural modelling and PolyPhen-2 software. Combined, the observations of this and the previous section indicate inherent instability of (MACMU)NAT1 variants as the likely cause for reduced enzymatic activity.
Each set contained a preparation of (MACMU) NAT1_1 recombinant protein, as reference, plus one or two of its variants. Duplicate reactions were performed with acetyl-CoA as donor substrate and p-aminobenzoic acid (PABA), 5-aminosalicylic acid (5AS), p-anisidine (PANS) and sulphamethazine (SMZ) as acceptor substrates. Residue p. Leu89 is located inside the enzymatic core, on β2-strand of β-barrel domain II of the protein molecule  64. Substitution to Phe is predicted by PolyPhen-2 as probably damaging, with the highest score (Table  2 ). The same tool identified p. Leu89 as essentially conserved across NAT1 homologues in the UniProt database. The residue is located directly behind β4-strand carrying p. His107 of the catalytic triad and its adjacent p. Ile106 that is important for binding of NAT1-selective aminobenzyl substrates, like p-aminosalicylic acid and PABA  64.
Phe125-p. Arg127-p. Tyr129 of the important catalytic pocket loop 125-129. In a, the "mesh" vs. "sticks" representation was preferred for polymorphic residue p. Gly51Ala, to better illustrate the difference in space occupied by the side chain of each amino acid. In c, d, f and g, additional residues or structural elements of relevance are coloured red and marked accordingly. In d and g, the solid black lines indicate important interactions involving the polymorphic residues. The protein was modelled against the known crystallographic structure of (HUMAN)NAT1 (PDB ID: 2PQT)  64. selective pressures, but also by the lifestyle choices of our species. The NAT enzymes metabolize toxic aromatic amines found in cooked food and other byproducts of everyday human activity since prehistoric times. Variability in NAT genes has thus been studied in the context of understanding how subsistence patterns may have affected the genetic makeup of human populations  58, 59, [98] [99] [100] [101] [102] [103].
Generation of polymorphic (MAcMU)NAT1 constructs. To date, the NAT1 homologues of three macaque species (M. mulatta, M. sylvanus and M. fascicularis) have been cloned and characterized  61, 107  , and those encode identical polypeptide chains. The (MACMU)NAT1 reference sequence (Nucleotide ID: KU640969. 1) of the rhesus macaque was already available 61 in pET28b(+) expression vector (Novagen) for the purposes of this study. Incorporation of each non-synonymous SNV found in the various (MACMU)NAT1 haplotypes was performed by site-directed mutagenesis using the QuikChange II kit (Agilent), as previously described  74. The mutagenised constructs were verified by sequencing of the insert (GATC Biotech, Konstanz, Germany).
Primate sequences for NAT1 were retrieved from the Genome Database (https://www. ncbi. nlm. nih. gov/genome/), using BLASTn, and the search for repetitive elements downstream of NAT1 gene was carried out with RepeatMasker open version 4. 0. 8 (http://www. repeatmasker. org). The presentation of SNVs in the manuscript is according to the guidelines of the Human Genome Variation Society (http://www. hgvs. org/mutnomen/) and each haplotype was named following the recommendations of the NAT Gene Nomenclature Committee 111 (http://nat. mbg. duth. gr/). The reference allele  61  (Nucleotide ID: KU640969. 1) was assigned symbol (MACMU)NAT1*1 and the remaining haplotypes were named in the order of their annotation during the study. A GenBank accession number (Nucleotide ID) was assigned per haplotype.
Structural alignment of protein sequences was carried out with T-COFFEE Expresso (http://tcoffee. crg. cat/apps/tcoffee/do:expresso) and graphically visualised with ESPript3. 0 (http://espript. ibcp. fr). Modelling of (MACMU)NAT1 was performed against the known structure of human NAT1 protein (PDB ID: 2PQT)  64  , using the Swiss-Model online tool (http://swissmodel. expasy. org/). Graphical visualisation of models was performed on PyMOL (Schrödinger, LLC). The effect of each polymorphism on (MACMU)NAT1 protein was also predicted using PolyPhen-2 (HumDiv model, http://genetics. bwh. harvard. edu/pph2/), an online bioinformatics tool assigning impact probability scores in the scale from 0. 00 (benign) to 1. 00 (probably damaging). The potential consequences of amino acid substitutions were further assessed according to Russell and colleagues  95  (http:// www.
Patients who failed conservative medical therapy, defined as either having a VAS back pain score ≥50 mm at 6 weeks after initiation of fracture care or a VAS back pain score ≥70 mm at 2 weeks after initiation of fracture care, were considered eligible. Patients must also have had an Oswestry Disability Index (ODI) score ≥30%. The list of all selection criteria is available in Table  1. The study protocol was approved by relevant ethics committees before implementation. All study procedures complied with the International Conference on Harmonization Good Clinical Practice (ICH GCP E6 R2), the Declaration of Helsinki, ISO 14155, and all applicable countryspecific regulations. All eligible patients provided written informed consent before participating in any study-related activities. The study is registered with ClinicalTrials.
Sensitivity analyses were also conducted to evaluate the impact of missing data by first treating all missing data as failures and then as successes. The additional composite endpoint and midline target height restoration at 6 and 12 months were tested for superiority versus control on both ITT and PP populations, using a t test with one-sided 2. 5% alpha. All secondary endpoints were compared between groups using appropriate bilateral statistical tests at the 5% significance level. All analyses were performed using SAS System version 9. 4.
Acknowledgments This study was sponsored and funded by  Vexim SA. The authors thank the patients who consented to participate in the SAKOS study and all participating staff at the investigational centers. The authors wish to acknowledge and dedicate this manuscript to  David Noriega , whose contributions as study coordinator were invaluable. The authors thank  Marie-Pierre Hontas  (Director Scientific and Clinical Affairs),  Christine Cachau  ( Manager Scientific  and Clinical Affairs),  Jessica Ward  (Senior Clinical Affairs Manager -Stryker-), and  Brigitte Mazein  (freelance medical writer) for initial review of the data, writing of the clinical study report and manuscript. The authors thank  Frank Trautwein  and  Marcel Dreischarf  ( ACES, Filderstadt, Germany ) for providing their services as independent radiographic core lab.
pneumophila Spl shows indeed sphingosine-1 phosphate lyase activity like its human counterpart  (45). Furthermore metabolomics analyses of L. pneumophila infected human macrophages revealed that L. pneumophila LpSpl targets the sphingolipid metabolism of the host cell directly to modulate the levels of sphingosine and restrains autophagy  (45). Thus, LpSpl is an enzyme that modulates the host cell sphingolipid metabolism to the pathogens advantage. The question arises "what is the origin of such an eukaryotic enzyme in an prokaryotic genome?". To answer this question we have undertaken phylogenetic analyses of this gene by recruiting homologous sequences from a database containing only completed genome sequences. Selected representatives of all eukaryotic groups and one representative of each bacterial species were included in the analyses.
Statistical analysis Between-group differences were analyzed using Mann-Wilcoxon U-test. Pearson's product moment correlations were calculated to evaluate relationships between variables. For all statistical tests, a p value of less than 0. 05 (Benjamini-Hochberg adjusted, otherwise known as False Discovery Rate (FDR) corrected) was considered statistically significant. Principal Component Analysis (PCA) was performed to provide a broad view on a dataset, emphasizing its most valuable informationwise subsets. PCA reduces intricate multiple-dimension data into several (usually two, less often three or more) most meaningful Principal Components in terms of information carried by them by maximizing the variance among the components. In this study Principal Components (eigenvectors and eigenvalues) were calculated using R built-in prcomp function with scale parameter set True; corresponding charts were built using ggbiplot R package.
The size of the bubble was inversely proportional to the p value mentioned above. All the axes as well as the size of the bubbles were plotted using logarithmic scale. Structural 3D plots were built to convey information about the structure of fatty acid residues of affected lipid species, notably the number of double bonds and the length of carbon chains. Each dot represented a single molecular species, with a number of double bonds displayed along the X axis and a number of carbon atoms in the fatty acid chains displayed along the Z axis. The Y axis enlisted all five HDL subfractions. Only molecular species that revealed statistically significant differences between the patient and control groups were shown in the plots. Network maps offer a graph-type visual representation of all intrinsic correlations within a dataset.
Color of the node indicated lipid class each species belonged to, as for the bubble plots described above. All lipid species from all HDL subpopulations were employed to build network maps. To correlate the lipidomic data with the functional metrics, the species abundances were recalculated on the HDL concentration basis employed to evaluate a given metric (phospholipid for cholesterol efflux capacity and total mass for antioxidative activity). Affected metabolic pathways were identified and included in classical pathway representation after identification using the MetExplore web-based tool  [20]. The metabolic network Homo sapiens (strain: Global Network, source: KEGG Map, Version 24/08/2017) of human species was based on 88 metabolic pathways including 1572 metabolites  [21]. Metabolic networks are directed graphs, so it is possible to calculate compound importance based on relative betweenness centrality and out-degree centrality of any given compound from a pathway.
PCA When PCA was applied to our dataset, the control and patient groups were entirely separated for the lipidome of both small, dense (Fig. 1 ) and large, light (Supplementary Figure  S1 ) HDL subpopulations on a plane of the first two Principal Components, indicating that individual components of the HDL lipidome were strongly altered in apoA-I-deficient subjects. Indeed, for all five HDL subpopulations, the amount of variance explained by the first two Principal Components was higher than 45%, consistent with marked differences between the groups.
Lipid-related genes affected by apoA-I deficiency The list of lipid species whose abundances were correlated with the biological activities of HDL and were significantly differing between the groups (FDR-corrected) was further employed to identify lipid-related genes affected by apoA-I deficiency. For this purpose, the species names were converted to their corresponding Human Metabolome DataBase (HMDB) accessions and entered into the MetaboAnalyst Network Explorer tool. The analysis revealed seven genes being associated with impaired lipid metabolism in apoA-I deficiency, namely APOA1, LCAT, CDS1, CDS2, GLTP, PLA2G1B, and PCSK7 (Fig. 7 ).
Genotype D is predominant in Northern Africa  [13]. Yet, data on the molecular characteristics of HBV in adults living in DRC are very limited. Some studies have been performed on very small sample sizes of adults  [14] [15] [16]. Other studies included either symptomatic patients with jaundice from different provinces of the country  [17]  or adults living in the eastern part of DRC  [18, 19]. The present study was performed in asymptomatic adults in Kinshasa, the capital city of DRC, located in the western part of the country.
Phylogenetic analyses The nucleotide sequences obtained were submitted to GenBank and were given the accession numbers from KR535608 to KR535622 (Table  S1 ). A similarity search at GenBank nucleotide database was conducted using the Basic Local Alignment Search Tool for nucleotides (BLASTn)  [22]. The new HBV sequences from Kinshasa along with 64 representative strains of genotypes A to H (Fig. 1 ) were aligned using Clustal W, and a phylogenetic tree was constructed using the Molecular Evolutionary Genetics Analysis software (MEGA6. 05) by the Maximum Likelihood method and the Tamura three-parameter model of nucleotide substitution  [23]. Bootstrap testing of phylogeny was inferred following 1000 replications to assess the reliability of the clusters. Values equal to or greater than 50 were indicated on the branches.
From the 18 DNA samples, 14 were selected for sequencing, based on quality of PCR products. Out of the 14 PCR products subjected to nucleotide sequencing, 13 were successfully sequenced. Both DNA detected in the two positive controls were also successfully sequenced. Thus, a total of 15 nucleotide sequences were obtained and were named beginning with "DRC-Kin" followed by study identification numbers or letters, e. g. DRC-Kin-9126519 or using their accession numbers (Table  S1 ). Ten (66. 7 %) out of the 15 new nucleotide sequences clustered into genotype A. Four new nucleotide sequences (26. 6 %) clustered into genotype E. One (6. 7%) nucleotide sequence clustered into genotype D. The new HBV/A sequences belonged to quasi-subgenotype A3 (5/10), subgenotype A4 (3/10) and subgenotype A1 (1/10).
Acknowledgments We appreciate the excellent technical assistance of  Christopher Kasanga  from  Sokoine University of Agriculture, Morogoro, Tanzania ,  Donatien Kayembe  and  Mireille Nganga  from the  University Clinics of Kinshasa, Kinshasa ,  DRC , of  Paul Masiangi from the School of Public Health in Kinshasa, DRC , and of  Eddy Sokolua  from the  CNTS, Kinshasa ,  DRC.
Introduction Desert dust concentrations raise concerns about adverse effects on human health. During the last decade, special attention has been given to mineral dust particles from desert dust and sand storms. However, evidence from previous reviews reported inconclusive results on their health effects and the biological mechanism remains unclear. We aim to systematically synthesise evidence on the health effects of desert dust and sand storms accounting for the relevant desert dust patterns from source areas and emissions, transport and composition. Methods an analysis We will conduct a systematic review that investigated the health effects of desert dust and sand storms in any population. The search will be performed for any eligible studies from previous reviews and selected electronic databases until 2018.
Study selection and reporting will follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Data from individual studies will be extracted using a standardised data extraction form. Quality of the studies will be assessed using a risk of bias tool for environmental exposures developed by experts convened by the WHO. A meta-analysis will be performed by calculating the appropriate effect measures of association for binary and continuous outcomes from individual studies. Subgroup analyses will be performed by geographical areas to account for desert dust patterns. Ethics and dissemination No primary data will be collected. For this reason, no formal ethical approval is required. This systematic review will help to fill the research gaps in the knowledge of desert dust on human health.
Information sources We will systematically conduct a comprehensive literature search. 0] [11] [12]  Next, we will conduct a literature search for studies matching the study eligibility criteria in various databases, including MEDLINE (using PubMed) and EMBASE. Finally, references of identified relevant studies will be scanned to identify additional published data matching the study eligibility criteria. The search strategy has been developed in collaboration with a health sciences librarian specialising in systematic search procedures. We used free-text for each search engine, considering different definitions and keywords for desert dust and sand storms and health outcomes (online supplementary file 2). We will retrieve eligible published studies until 2018 without language restrictions and will translate the non-English abstracts and studies, if necessary.
Data extraction Reviewers will use a standardised data extraction form using Excel spreadsheet (Microsoft Corporation). Here, if a given study reports several health outcomes of interest, each outcome will take one record. Likewise, if a given study reports age or gender-specific effects, these will Open access also be collected. Moreover, if a given study reports more than one effect estimate for each health outcome (eg, for several lags), the one that the authors favoured most will be extracted. Main conclusion/findings for each study will also be collected as free text. The following characteristics of the included studies will be extracted: 1. Citation details (eg, first author, title, journal and date of publication). 2. Study location (eg, city, country, region), study period, characteristics of the study population (eg, age, gender).
Quality assessment We will use a new domain-based risk of bias tool to assess the quality of the evidence across studies for associations between specific air pollutants and adverse health outcomes. The risk of bias tool, currently under development by a group of experts on environmental exposures convened by the WHO Guideline Development Group (GDG) for the AQGs, will adapt the domains according to the needs of the observational studies of exposure that form the body of the evidence in air pollution. It will be revised and adapted to by the systematic review team members, and a member of the WHO GDG will support the application of the tool. Two reviewers will assess the risk of bias independently, and disagreements will be resolved through discussion. data synthesis and statistical analysis For binary outcomes (eg, mortality and morbidity), the relative risk (RR) will be used as the standard effect measure of association across studies.
The HR if reported, may be considered equivalent to RRs. If ORs are reported in a given study and the outcome prevalence is >10% they will be approximated as RRs  15  and similarly if the study design allows for it (eg, time-series and time-stratified case-crossover  16  ). Meta-analysis input data will be as the percentage increase in the RR (eg, as %RR = (RR-1) ×100%). When reporting effects of particulate matter for days with and without dust events the %RR will be standardised for a given increment of 10 µg/m 3 , assuming a linear exposure-outcome relationship. For continuous outcomes, effect measures such as weighted mean difference (WMD) or standardised mean difference (SMD) may be used. The scale of the available data will be primarily used to determine the choice of effect measure if studies report effect estimates with the same or similar scale (WMD) or when the outcome is measured using different scales (SMD).
15 e measures of the association from individual studies will be summarised in a meta-analysis estimate, with its 95% CI, based on fixed or random-effects model, depending on the heterogeneity of the analysis. 15 17 Statistical heterogeneity of the effect estimates between studies included in the meta-analysis will be assessed using the I 2 statistic, where values of 25%, 50% and 75% will be considered as a low, moderate and high degree of heterogeneity, respectively. 18 We will also report the between-study variance (τ 2 ) and the Cochran Q test for heterogeneity. 19 All analyses will be done using Stata Statistical Software V. 15. Due to the relevance of the study setting and dust exposure assessment, subgroup analyses will be done by geographical areas, considering studies conducted in Eastern Asia, Europe, Middle the East and other regions (eg, Australia and North America).