dataesr/scientific-paragraphs-categorization huggingface dataset:
gov), and (3) conference abstracts (from 2015-2021 for the American Society of Hematology and from 2015-2022 for the American Society of Clinical Oncology and the European Hematology Association). We imported citations from all databases into an EndNote X9 database. After removing duplicate articles, two independent reviewers (H. M. and A. M. ) screened the remaining citations. We included the most recent analysis if studies had multiple interim analyses or abstracts. The same two team members (H. M and A. M) reviewed full text to confirm eligibility for any citation deemed potentially relevant. If there were disagreements, the article was reviewed by a third reviewer (T. W).
Selection criteria We used the following eligibility criteria for included studies: (1) included an evaluation of therapeutic drug agent for newly diagnosed (NDMM), or relapsed/refractory (R/R) patients (2) was a clinical trial (phase I to IV, we excluded real-world observational cohort and registry database studies) (3) reported on a measure of frailty (intermediate fit or frail) either as inclusion criteria for trial entry, baseline characteristics or post-hoc analysis. We defined frailty measures as any screening or comprehensive geriatric assessment tools which included ≥2 aging-associated domain assessments. These domain assessments could include a combination of age, comorbidities, functional/performance status  [17]. We excluded any study that classified frailty based solely on one factor alone (i.
Data extraction We extracted study data, including first author, years of trial enrollment, trial phase (I, II, or III/IV), study methodology (randomized controlled trial [RCT] vs. non-RCT), disease phase (NDMM vs. R/R), sample size, trial location, and therapeutic agents in both the experimental and control arms. The assessment tool utilized for frailty assessment was recorded. A study was recorded as using the IMWG frailty score if age, Charlson comorbidity index, ADLs and IADLs were assessed or if the study selfreported as using IMWG frailty Index  [9]. A study was recorded as using the simplified frailty score (also known as modified IMWG frailty index) if ECOG PS was used instead of ADLs and IADLs  [10]. We also recorded the frailty categorization (two or three subgroups), frailty prevalence, patient characteristics (median age) and outcome data (efficacy and toxicity data).
RESULTS Of 3193 studies, we included 257 in the full-text review (Fig. 1 ). After a full-text review, 43 clinical trials met the eligibility criteria for inclusion in this review. Common reason for exclusion during the full-text review included ineligible study design such as observational cohort or database registry studies (62/257, 24. 2%) and not assessing or reporting on frailty (36/257, 14. 1%).
Unfortunately, existing frailty tools, including the IMWG frailty score, consist of largely static variables such as the categorical chronological age and pre-existing comorbidities and may be less suited for detecting changes in frailty over time. We do not yet know if frailty is modifiable or whether longitudinal changes in frailty will further optimize our treatment delivery; however, further development and validation of frailty assessment tools that are both sensitive, specific, and responsive to changes in frailty over time will be essential in the future evaluation. The strength of this analysis includes the first comprehensive review highlighting the impact of frailty in therapeutic MM trials. We included randomized controlled trials and single-arm therapeutic MM trials. We used a comprehensive search and screening procedure with careful data abstraction.
NMR Experiments 2D 1H-NOESY, 2D 1H-TOCSY, 15N-HSQC and 13C-HSQC spectra (15N and 13C natural abundance) were recorded at 293 K on an Avance III HD BRUKER 700 MHz spectrometer prepared with a cryoprobe. 1H chemical shifts were related to the water signal (4. 821 ppm at 293 K). NMR data were processed using Bruker's Topspin 3. 2TM and analyzed with CCPNMR (version 2. 2. 2)  [20].
Structure Calculations Structures were calculated using CNS  [21, 22]  through the automatic assignment software ARIA2 (version 2. 3)  [23]  with NOE-derived distances, hydrogen bonds (when the typical long or medium distance NOE cross peaks network characteristic of β-sheets had been observed-HN/HN, HN/Hα, Hα/Hα), backbone dihedral angle restraints (assessed with the DANGLE program  [24] , see Supplementary Table  S1 ) and three disulfide bridges (Cys3-Cys24/Cys10-Cys26/Cys18-Cys36). The ARIA2 procedure used simulated annealing with torsion angle as well as Cartesian space dynamics with the default settings. The iterative protocol was completed until the assignment of the NOE cross peaks was achieved. The last run was performed with 1000 initial structures out of which 200 structures were refined in water.
The 3D structures were calculated by taking into account a total of 920 NOEderived distance restraints, added to 8 hydrogen bonds and 56 dihedral angles (Table  1 , Table  S1  and Figure  S1 ). PA1b was shown to belong to the knottin structural group  [32, 33]  based on NMR structural data  [34] , and the accuracy of its disulfide pairing was later confirmed by racemic crystallography  [35]. Given that AG41 shares 73% similarity with PA1b (Figure  1 ), the same connectivity pattern was used for the structure calculation, namely Cys3-Cys24, Cys10-Cys26 and Cys18-Cys36. Among the 200 water-refined structures of AG41, the 10 structures of lowest total energy compliance with all the experimental data and the standard covalent geometry were used for statistical analysis (Table  1 ). Restraints and coordinates were deposited as PDB entry 8AHK.
Supplementary Materials: The following supporting information can be downloaded at: www. mdpi. com/xxx/s1. Table  S1 : Dihedral angles used during the structure calculation of AG41 and predicted by DANGLE, which predicts protein backbone dihedral angles and secondary structure assignments solely from amino acid sequence information, experimental chemical shifts and a database of known protein structures and their associated shifts Table  S2 : Characterization of the oxidized form of AG41 and its mutants by RP-HPLC and MALD-TOF MS and Figure  S1 : Interaction Matrix of the NOE-derived restraints used during the structure calculation within all the residues.
Supplementary Materials: The following supporting information can be downloaded at: https: //www. mdpi. com/article/10. 3390/biom13030446/s1. Table  S1 : Dihedral angles used during the structure calculation of AG41 and predicted by DANGLE, which predicts protein backbone dihedral angles and secondary structure assignments solely from amino acid sequence information, experimental chemical shifts and a database of known protein structures and their associated shifts; Table  S2 : Characterization of the oxidized form of AG41 and its mutants by RP-HPLC and MALD-TOF MS; Figure  S1 : Interaction Matrix of the NOE-derived restraints used during the structure calculation within all the residues.
However, their use is not encouraged in the early stages of COVID-19 infection as they cause suppression of the immune system  [215]. In this context, a large open-label, multicenter, randomized trial was conducted in the United Kingdom (UK), including patients with suspected or laboratory-confirmed COVID-19  [216]. The sample consisted of 2104 patients, divided between the group that received 6mg of dexamethasone once a day for ten days and 4321 patients that received only the usual care. At the end of the study, it was observed that dexamethasone reduced mortality in 28 days in patients with symptoms longer than seven days. Therefore, the regimen was put into clinical use in the UK  [216]. On the other hand, using non-steroidal anti-inflammatory drugs (NSAIDs) remains controversial in treating COVID-19, as little data are available on the subject  [217] [218] [219] [220] [221] [222] [223] [224].
Non-targeted metabolomic analysis using molecular networking was successfully used to investigate both wood and fungal SMs  [28, 29]. Molecular networking was first introduced in 2012  [30]  and is a tool using tandem mass spectrometry (MS/MS) to visualize the diversity of a complex mixture of metabolites. Similar methodologies were already used mainly for protein annotation and pathways identifications, for example, with the open-source software Cytoscape  [31]. The principle relies on the fact that two compounds that have similarities in their structure will have common fragment ions and/or neutral loss between their parent and fragment ions  [32]. MS/MS spectra are compared using their cosine score and a clustering algorithm  [33]. The cosine score indicates the degree of similarity between two MS/MS spectra, considering their parent and fragment ions  [32].
Global Natural Products Social Molecular Networking (GNPS, https://gnps. ucsd. edu/, accessed on 26 April 2023) is a community providing tools and sharing databases for molecular network analyzes and annotations  [34]. The open-source software MetGem (https://metgem. github. io, accessed on 9 March 2022) is an interface for applying this method and using the available databases  [35]. MetGem proposes not only the molecular network visualization but also the t-distributed Stochastic Neighbor Embedding (t-SNE) algorithm, which preserves distances between clusters, among other advantages  [35]. This method was successfully used for the characterization of new metabolites from microorganisms with the help of complementary techniques such as nuclear magnetic resonance  [28, 29]. The aim of this study is to describe Fmed metabolism depending on the presence of different wood species, including grapevine wood.
Network Metabolomic Analyses MSconvert software belonging to Proteowizard package  [39]  was used for data conversion from Agilent constructor format to mzML files. Data pretreatment was performed with MZMine v. 2. 53 software  [40]  with following parameters: mass detection noise level was set to 1000 for both MS and MS/MS; chromatogram building was achieved with the automated data analysis pipeline (ADAP) chromatogram builder algorithm  [41] , with a minimum of 3 scans per group, a minimal intensity of 10,000 and m/z tolerance at 0. 005 (or 10 ppm). Chromatogram deconvolution was achieved with ADAP wavelets algorithm  [41]  with the following parameters: signal/noise threshold 20, minimum feature height 10,000, coefficient threshold 10, peak duration range between 0. 00 and 1, RT wavelet range from 0.
02 to 0. 09, m/z center calculated as median, m/z range for MS/MS 0. 1 and RT range for MS/MS 0. 2. Feature lists were deisotoped with isotopic peaks grouper algorithm with an m/z tolerance at 0. 005 or 10 ppm, and a RT tolerance at 0. 1 min. Peak alignment was performed using the join aligner method with m/z tolerance at 0. 005 or 10 ppm, m/z weight set at 80% and RT tolerance at 0. 1 min with a weight of 20%. Aligned chromatograms metadata were used for statistical analyses first with all MS features. Then, MS/MS data were then filtered, and chromatograms and metadata with only MS/MS data were exported to GNPS-FBMN compatible mgf and csv files. Files were processed with MetGem 1. 3. 6 software  [35, 42]  (https://metgem. github. io/, accessed on 9 March 2022) with following parameters: m/z tolerance was set at 0.
04 and a minimum of two matched peaks was set for similarity between two nodes. All peaks in a +/-17 Da window around the m/z precursor were removed. Only peaks above 1% of the highest or the six highest peaks in a +/-17 Da window throughout the spectrum were kept. t-SNE visualization was constructed with following parameters: at least 1 cosine score above 0. 75 was requested. Number of iterations, perplexity, learning rate and early exaggeration were set to 10,000, 6, 10 and 12, respectively. To cope with the high number of MS/MS features, Barnes-Hut approximation angle was used and set at 0. 7. Annotations were performed with GNPS (https://gnps. ucsd. edu/ProteoSAFe/libraries. jsp, accessed on 19 October 2022) and MS-DIAL (http://prime. psc. riken. jp/compms/msdial/main. html#MSP, accessed on 19 October 2022) public standards databases.
0 buffer, 5 µL of ultra-pure water (Millipore ©, Burlington, MA, USA, Merck™, Darmstadt, Germany); 5 µL of GSTO2S purified from Trametes versicolor as described in  [45] , a at final concentration of 20 µM; 2 µL of SYPRO orange diluted at 1. 25%; and 2 µL of extract at initial concentration of 10 mg/mL in DMSO (or 2µL of DMSO only for control wells) were added in one well of a 96-well microplate, on ice and in triplicate. Microplates were spun at 2000 rpm and placed into a ThermalCycler CFX Opus 96 (Bio-Rad ©, Hercules, CA, USA). ThermalCycler temperature was controlled by BioRad CFX Maestro software and set to increase from 5 • C to 95 • C at 5 • C/min in 2 h. Fluorescence was measured continuously at excitation 485 nm and emission 530 nm with HEX fluorochrome. The denaturation temperature, corresponding to the temperature where 50% of the highest fluorescent is measured, was obtained by calculating the derivate of the curve of fluorescence as a function of the temperature.
Statistical Analyses Data analyses were performed using R v. 2022. 02. 3 software (PBC, https://www. r-project. org/, accessed on 9 March 2022). The Shapiro-Wilk test was used for a data normality check when relevant. Kruskal-Wallis with the Nemenyi Tukey multi-paired comparison test was used for nonparametric tests and Student Newman-Keuls (SNKtest) with Tukey multi-paired comparison test was used for parametric tests. Significant differences are indicated by *** for p-values < 0. 001, ** for p-values < 0. 005 and * for p-values < 0. 05. Principal component analysis (PCA) on the MS and MS/MS features obtained after MZMine v. 2. 53 pretreatment was conducted with R and Factoshiny package, and heatmaps were constructed with R.
Nodes were gathered into 91 clusters with the MetGem clustering algorithm set with a minimal of five features per cluster. As t-SNE algorithm has the particularity to keep distances between all nodes even between different clusters, it should be kept in mind that close clusters might gather features with some common fragment peaks or neutral loss. Annotation was achieved according to free GNPS and MS-DIAL databases, and cosine score (cs) was given as a confidence indicator. The closer the cs is to one, the more similarities there are between databases standards and experimental features. clusters constructed with MetGem. Circles were constructed manually according to GNPS and MS-DIAL databases annotation results. Nodes colors correspond to the relative proportions of each feature in the sample groups: non-degraded grapevine, beech and oak wood, respectively, correspond to control grapevine, beech and oak wood extracts; degraded grapevine, beech and oak wood correspond to extracts of woods after exposure to all three strains; for each strain, mycelium grown on the wood group corresponds to all mycelia grown on the three wood species.
Differences between grapevine wood samples after exposure to fungal degradation are detailed in Supplementary Materials Table  S4  (which includes features found in other wood species samples). In grapevine control wood only (Table  S3 ), six features were mapped in clusters 11 to 14. Two of them were annotated with an in silico database (ISDB) as "compound 1", with a structure close to epsilon-viniferin (cs 0. 55 and 0. 60, m/z 453. 133, cluster 14, Figure  S7 ), and two others either as s-viniferin (cs 0. 78, m/z 455. 149, cluster 13) or as an analog of the latter. Other features found only in grapevine control wood were annotated as coniferaldehyde (cs 0. 94, m/z 179. 070, cluster 75), epicatechin (cs 0. 93, m/z 291. 086, noise), 3,4,5-trimethoxyphenol (cs 0. 93, m/z 185. 081, noise), syringaldehyde (cs 0.
Mycelium Samples Metabolome Considering mycelium samples, fungal species were the most discriminant factor, and differences among wood species were also observed (see Section 3. 4). Among the 2 005 MS/MS features found in mycelium samples, 541 were only in Fmed mycelium, and 418 only in Tver mycelium (Table  S2 ). MS/MS features found in Fmed mycelium only are listed in Supplementary Materials Table  S5. Some of them (227 features) were found only in Fmed mycelium grown with grapevine wood, only with beech wood (213 features) or only with oak wood (70 features). However, a high variability between individuals was observed. Only one feature, which was not annotated with the databases, was found in all individual Fmed mycelia grown with beech wood (m/z 491. 097, cluster 16, RT 6. 95 min). Regarding mycelia grown on grapevine wood, one sample ("R124M1"), accordingly to PCA analysis, was markedly different from the others, containing more features.
For all sample names, "X" refers to each biological repetition for wood and mycelia extracts, and to technical replicate for quality controls and blanks;   S1 : NSAF values for all selected secreted proteins (emPAI above 1. 00) in (a) Fmed phco36 and LR124 and (b) Tver secretomes; Table  S2 : MS/MS features selected after data pretreatment with MZMine 2. 53, mapped with t-SNE algorithm and annotated after database search with MetGem; Table  S3 : Selected MS/MS features specific to wood species (features found only in one of the three wood species); Table  S4 : Selected MS/MS features in grapevine wood samples: features found in grapevine wood after exposure to none or only one of the two fungal species; Table  S5 : Selected MS/MS features in Fmed mycelium samples: features found in Fmed mycelium depending on its substrate; Table  S6 : Selected MS/MS features in Fmed and Tver mycelium samples: features found in different abundances in Fmed and Tver mycelia grown with grapevine wood; Table  S7 : Selected MS/MS features in Fmed and Tver mycelium samples: features found in different abundances in Fmed mycelia grown with grapevine wood compared to beech or oak wood.
Match score as analogs was 0. 82; Figure S9: MS/MS spectra of feature m/z 348. 275 (experimental) compared to methyl 2-ethyl-4-[(3R,4R,5S)-5-hydroxy-4,5-dimethyl-2-oxooxolan-3-yl]-2-methyl-3-oxobutanoate (m/z 304. 176) spectra from the database, and structure of the standard (©GNPS). Match score as analogs was 0. 59; Figure S10: MS/MS spectra of feature m/z 277. 180 (experimental) compared to nerylacetate (m/z 197. 154) spectra from the database, and structure of the standard (©GNPS). Match score as analogs was 0. 80; Figure S11: MS/MS spectra of feature m/z 330. 264 (cluster 69) compared to spectra from allo-protolichesterinic acid [M + NH4] database (m/z 342. 364), and allo-protolichesterinic acid structure (ISDB-©GNPS). Match score as analogs was 0. 68; Figure S12: MS/MS spectra of feature m/z 407.
Data Availability Statement: Not applicable. Acknowledgments: This work benefited from the ASIA platform (Université de Lorraine-INRAE; https://a2f. univ-lorraine. fr/en/asia-2/). Proteomics analyses were performed on the PAPPSO platform (http://pappso. inra. fr) which is supported by INRAE (http://www. inrae. fr), the Ile-de-France regional council (https://www. iledefrance. fr/education-recherche), IBiSA (https://www. ibisa. net) and CNRS (http://www. cnrs. fr). The authors would like to thank Francis Groff and the Valentin Zusslin estate (F-68500, Orschwihr) for providing grapevine wood, Bernd Zeller from (BEF, INRAE) for beech and oak wood, and Alexis Boutillat for his contribution.
This makes it possible to investigate the complex interplay between genetics, environment, and evolution of a population across time  [27, 28]. In genomic studies, the comparison of data from herbaria and modern samples can provide valuable information for optimizing the management of plant genetic resources  [29]. Cozzolino et al. (2007)   [30]  evaluated changes in the genetic diversity of the Anacamptis palustris population because of habitat transformation in Italy over time. To do so, authors sequenced and compared a plastid region between herbarium specimens collected prior to the Second World War and modern samples. Thus, this approach could be very useful as a complement to large genotyping and phenotyping programs of modern accessions aimed at understanding the environmental adaptation and the evolutionary dynamics of genetic diversity, such as the INCRESE project  [31] , which is focused on food legumes such as chickpeas  [32] , the common bean  [33] , lupin  [34] , and lentil  [35].
Informed consent was obtained from all participants in the study and the study was approved by the Scientific Ethical Committee of Rennes Hospital in France (Avis n • 22. 185). Data from a previous interventional study  (David et al. , 2021a)  were also used to compare the excretion rate of APAP metabolites with those obtained during the first longitudinal observational study. Briefly, the study recruited 4 healthy men aged 30-60 years at the Department of Pharmacy and Department of Biology, University of Copenhagen. Exposure to APAP was performed over four days with an administration of 1 g of APAP on the third day. Urine samples were collected +1 h, +2h (urine only), +4h, +6h after intake, and in the morning of the subsequent two days (+24 and 48 h, urine and blood plasma). All individuals provided informed consent to participate in the study.
Targeted analyses of APAP in urine samples Targeted analyses were used to analyse urine samples from the crosssectional observational study (section 3. 2) to identify pregnant women with APAP concentrations within therapeutic doses (and compare these data with questionnaires and non-targeted results). APAP concentrations analyzed as free APAP after hydrolysis of β-glucuronide conjugates (i. e. , corresponding to free APAP + APAP glucuronide) using a LC-MS/MS method were measured for each pregnant woman and are represented in ng/ml. Twenty µL of urine were transferred to a glass insert (Teknolab Sorbent, Kungsbacka, Sweden) of a 96-well Rittner plate (Teknolab Sorbent). Ten µL of ammonium acetate (pH 6. 5) and 0. 01 mL glucuronidase (Escherichia coli, Roche Diagnostics (CAS No 9001-45-0), ≥80 U/mg protein) were added, and the solution was incubated at 37 • C for 90 min.
Centrifugation was performed at 4 • C and 13,300 rpm for 20 min, after which supernatants were collected and evaporated to dryness under vacuum. Samples were recovered in 80:20 (v/v) ultrapure water to acetonitrile mixture. All sample extracts were profiled using an Exon UHPLC system (AB SCIEX, USA) coupled to an AB SCIEX X500R Q-TOF-MS system (SCIEX, Canada), equipped with a DuoSpray ion source as described in  David et al. (2021a). As a first step, all the samples were analyzed in a full-scan experiment (50-1100 Da) in bothand +ESI modes. MS/MS mass fragmentation information for chemical elucidation was obtained by further analysis of selected samples in sequential window acquisition of theoretical mass spectrum (SWATH) data-independent acquisitions. The Q1 isolation window strategy was generated using the SCIEX Variable Window Table from previously acquired MS data on the specific sample of interest in order to optimize window widths across the entire m/z range.
Data processing and annotation The obtained LC-MS raw files (wiff2 format) were converted into mzXML format using MSConvert GUI (Palo Alto, CA, USA) using the Proteowizard open-source software  (Chambers et al. , 2012). Converted data were then imported to MATLAB computer and visualization environment (Release 2021b, The Mathworks, Inc. , Natick, MA, USA) and analysed with MSroi (Pérez-Cova et al. , 2021) chemometrics strategy using the MATLAB MSroi app  (Pérez-Cova et al. , 2021). This approach was employed for data compression, feature detection and filtering. In short, MSroi enables significant data compression in the spectral dimension without losing their instrumental spectral accuracy. The main parameters used for the regions of interest (ROI) procedure in this work were a mass error tolerance of 10 ppm, a minimum threshold value of 1000, and a minimum peak width of 10 s, and ROIs m/z values were calculated with the median of the m/z values determined for each chromatographic peak.