dataesr/scientific-paragraphs-categorization huggingface dataset:
Briefly, splenic, autoreactive T follicular helper cells initiate an autoimmune, antiplatelet antigen loop by inducing production of antiplatelet antibodies by B and plasma cells that further facilitate phagocytosis of opsonised platelets by macrophages in the reticuloendothelial system. These are the principal antigen-presenting cells that maintain the autoimmune loop over time. ] [15] [16] [17] [18]  In terms of the roles played by T cells in ITP, few studies have analysed the T cell subsets (or indeed the subsets of other lymphocytes) in peripheral blood of ITP patients,  [19] [20] [21] [22]  particularly in the context of RTX treatment. 18, 23 Thus, we retrospectively analysed data on peripheral blood lymphocyte subsets recorded for all ITP patients who received RTX and were followed-up in our centre.
Patient recruitment We retrospectively included patients with primary ITP on RTX followed-up at our centre from January 2007 to September 2021. All were at least 18 years of age and met the international consensus, primary ITP criteria. 24 Some evidenced only isolated biological autoimmunity, with no symptoms but were nonetheless considered to have primary ITP. All had failed first-line treatments with steroids and/or intravenous immunoglobulins (IVIGs). Additional exclusion criteria were thrombocytopenia not caused by primary ITP, a history of RTX treatment, prior splenectomy (because of the major role played by the spleen in ITP immune dysregulation), lost file data or no peripheral blood lymphocyte subset analysis prior to RTX, and/or concomitant treatment with a thrombopoietin-receptor agonist (TRA) that could interfere with the platelet response.
Data collection Patient data were anonymously extracted from medical files. We recorded age, sex, date of diagnosis, date of first RTX infusion, the date of the blood test that yielded the platelet count, platelet scintigraphy data (if any), previous treatments (steroids, IVIGs, disulone, hydroxychloroquine, TRAs) and the responses to those treatments, and the levels of antiplatelet, antinuclear and anti-phospholipid antibodies. All blood tests and labelled platelet scintigraphic tests were performed in departments of our hospital. The blood lymphocyte subsets were analysed as previously described  25  in the Bordeaux University Hospital Laboratory of Immunology employing a Beckman-Coulter FC500 flow cytometer. This yielded the total lymphocyte count and the percentages and numbers of CD3 - (mainly B and NK cells), CD19 + B cells, CD3 -CD56 + CD16 + NK cells, CD3 + T cells, CD4 + T helper cells, CD8 + cytotoxic T cells, CD3 + CD4 -CD8 -cells, CD3 + HLADR + activated T cells, CD3 + CD4 + HLADR + activated T helper cells, and CD3 + CD8 + HLADR + activated cytotoxic T cells.
Receiver operator curves were drown to determine sensitivities, specificities, and likelihood ratios, and for the relevant parameters, we chose the number with the best likelihood ratio + sensitivity + specificity. Statistical analyses employed GraphPad Prism ver. 6. 0 (Graph Pad Software Inc, Dotmatics) and R software version 4. 1. 0 (R Project for Statistical Computing, RRID:SCR_001905).
DISCUS SION In a selected population of 72 non-splenectomised patients who received RTX between 2007 and 2021 to treat primary ITP for whom peripheral blood lymphocyte phenotyping data were available prior to RTX, we found that a low number of circulating NK cells (<150/mm 3 ) could predict the response to RTX. In contrast to previous observations,  5, 7, 12  age, sex, duration of disease and the previous response to steroids did not predict the RTX response, possibly because of our strict selection process. The overall response to RTX at 6 months was 65%, in line with those of previous reports,  [4] [5] [6] [7]  and notably, 84% of CR patients still evidenced CR at 24 months. We also analysed the peripheral blood lymphocyte subsets after RTX, especially more than 6 months after therapy, to explore the persistent reorganization of the immune system after B cells reappeared.
Image acquisition. Images for immunolabelled cells on micropatterns were acquired with an inverted wide field Deltavision Core Microscope (Applied Precision) equipped with highly sensitive cooled interlined charge-coupled device (CCD) camera (CoolSnap Hq2, Photometrics). Z-dimension series were acquired every 0. 5 μm. Images for non-pattered immuolabelled cells were acquired with a spinning disk confocal microscope (Inverted Eclipse Ti-E (Nikon) + spinning disk CSU-X1 (Yokogawa) integrated with Metamorph software by Gataca Systems). Cells were imaged as Ζstacks with 0. 2 μm distance and 12 μm total height. Image processing and analysis. For cells on micropatterns, several tens of single cell images were aligned using the coordinates of the micropattern  17, 68  determined via an ImageJ-based macro (Bethesda, MD, USA).
The pipelines for different analysis were prepared as follows: To detect the total and membrane bound intensities of protein of interest (labeled as total integrated intensity or spots/total, respectively, in the figures) or intensities of co-localized proteins the pipeline was created as follows: Step 1: Module 'EnhanceorSuppressFeatures' was applied to channels where the objects needs to be segmented, either to obtain their intensities or objects for the intensities of co-localized proteins, to get sharp and defined objects which makes segmentation easier (for eg. On channels with LAMP1 or EEA1 or GFP-FYVE). Step 2: Nucleus was identified in the DAPI channel using the 'IndentifyPrimaryObject' module Step 3: Module 'IndentifyPrimaryObject' was used again on the images obtained from Step 1 to segments objects whose measurements are required (such LAMP1, EEA1, EGFP-FYVE) Step 4: Cells were segmented using the 'IdentifySecondaryObject' module with nucleus as the 'primary object' (identified in step 2) and using phalloidin or another cytoplasmic protein channel to recognize the cell boundaries.
The statistical analysis was a Kruskal-Wallis test with Dunn test for multiple comparisons correction. For all experiments, a large number of cells were monitored from 3 to 6 independent experiments. Two-sided Mann-Whitney U test were performed for 2 conditions comparisons. For multiple comparisons, a Kruskal-Wallis has been used with Dunn's test for multiple comparisons. Additionally, to compare the global distribution of cell population, χ² tests were performed (R function "chi-square()") and Benjamini-Hochberg multiple comparison correction has been applied. For the statistical analysis on the data from CellProfiler, GraphPad Prism 9. 3. 1 was used. Mann-Whitney U test or paired t test was applied for the two conditions comparison (indicated in respective figures) or Kruskall-Wallis test with Dunn test for multiple comparison.
Acknowledgements We thank  Jennifer Southgate  ( University of York, UK ) for her gift of NHU cell extracts and  Clémentine Krucker  and  Yann Neuzillet  ( Foch Hospital, France ) for deriving these cells. We thank  Elodie Chapeaublanc  for help on the bioinformatics and  Oliver Kepp  for critical reading of the manuscript. The authors greatly acknowledge the  Cell and Tissue Imaging Facility (PICT-IBiSA @Burg and @Pasteur)  and the  Nikon Imaging Center at Institut Curie (Paris)  that are member of the  French National Research Infrastructure France-BioImaging  ( ANR10-INBS-04 ). This project was supported by grants from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Skłodowska-Curie  grant agreement  n°666003  to PM; Capes/  Ciência sem Fronteiras/Process  ( 9121137 ) to CDBS; Grants from  Fondation ARC  pour la recherche sur le cancer to K.
S. and H. L. ,  Institut Curie SIRIC  grant to KS,  Agence Nationale de la Recherche  (# 2010 BLAN 122902 ), the  ITMO Nanotumor grant  to KS, the  Center National de la Recherche Scientifique and Institut Curie. The Goud team is member of the  Labex Cell(n)Scale  ( 11-LBX-0038 ) and the  Idex Paris Sciences et Lettres  ( ANR-10-IDEX-0001-02 PSL ). The Molecular Oncology team (FR) is supported by  La Ligue Contre le Cancer  ( Equipe labellisée program ).
Qualitative Observations via Confocal Imaging and Scanning Electron Microscopy After the last metabolic assay on day 7, all the samples were rinsed in phosphatebuffered saline (PBS) and fixed in paraformaldehyde (4% w/v) overnight. The surfaces where cells were seeded were covered with a permeabilization mix (BSA 2% [w/v], Triton 0. 1% v/v) for 2 h at 37 • C. A staining mix composed of DAPI (0. 1% v/v) and Alexa546conjugated phalloidin (0. 5% v/v) in a permeabilization mix was then applied for 2 h at 37 • C. All the samples were rinsed multiple times in PBS solution before the confocal microscopy acquisition (Leica TCS SPE). ImageJ  [28]  and Imaris (Oxford Instruments plc. , Abingdon, UK) software were used to perform the qualitative analysis of the acquisitions. The samples were dried in consecutive ethanol baths (30%, 50%, 70%, 90%, and 99%) with 1 h soaking steps and then coated with gold using a sputter coater (EMSCOPE SC500, Elexience, Verrieres Le Buisson Cedex, France) before the SEM (TM4000+ Hitachi, Tokyo, Japan) observations.
Cytoskeleton Orientation Assays To obtain a quantitative analysis of the cytoskeleton rearrangement, the Fiji plugin Directionality tool (freely available on GitHub https://github. com/fiji/Directionality, accessed on 4 May 2022) was used. First, hyperstacks of the height-through acquisitions were constructed. The sample matrix opacity-mediated autofluorescence was found to be equal in all the conditions observed. All the pictures were manually aligned according to the laser-carved patterns so that the trenches would be parallel (0 • and 180 • ) and perpendicular (90 • ) to the Fast Fourier Transform (FFT) plan. As SCAPs physiologically self-organize in a spindle shape with a narrowing space between the cells as the population grows, an increase in the signal was expected to be reached at a certain angle.
The control samples were then oriented with respect to this physiological feature, at 0 • and 180 • with respect to the FFT plan. To refine the orientation results, 180 steps of FFT were performed from -45 • to 134 •. All the results were then gathered and analyzed using GraphPad Prism version 8. 0. 0 software (GraphPad Software, San Diego, CA, USA) to generate the FFT heatmaps. Circular histogram frequency plots (with a frequency proportional to the area of each bar) were obtained through the adaptation of an open-source code from matplotlib. org (the version used for this article is available upon request and free of use).
Statistical Analysis All the tests were performed using GraphPad Prism version 8. 0. 0 software (GraphPad Software, San Diego, CA, USA). Non-parametric Kruskal-Wallis tests were selected, as the sample size was unsuitable for performing normality tests. The H0 hypothesis was rejected when the p-value was < 0. 05, and Tukey's post-hoc test was performed when H1 was accepted.
However, a noticeable difference was observed (Figure  4e ) in the behavior of the cells grown on samples with larger hatch distances compared with the cells grown on sanded bones. Indeed, The orientation of the actin filaments was analyzed via a Fast Fourier Transform (FFT) analysis of the actin filament channel obtained using confocal microscopy. The retrieved data were analyzed and translated graphically to a heat map (Figure  5a ) and frequency plots. On the heat maps, the frequencies are proportional to the intensity of the signal retrieved for each angle. A high frequency indicates a lot of signals from the actin filaments along that axis. The more cells aligned with a given angle, the strongest the signal and the higher the frequency obtained. The heat maps showed that, in the absence of laser patterning, the cells are preferentially oriented in a single range of angles with a single area of rise in frequencies.
For the sake of biocompatibility, the concentrations of each of the aforementioned substances in the present experimental study did not exceed their maximum allowed concentration, established by the Food and Drug Administration (FDA) and contained in the inactive ingredients database, for topical application in the form of a solution. 18 The resulting compositions of OCA and CPE, as well as their volume fractions and maximum allowed concentrations are shown in Table  1. As there is no information about maximum FDA-allowed concentration for topical application of glucose and sucrose, a concentration value (v/v) of 50% was established for our experiments as it was previously reported as the most effective concentration of glucose for OC. 39 In the case of oleic acid and DMSO, it was not possible to mix each of them (alone) with any of the three OCA since in this case the maximum allowed concentration for these chemicals would be exceeded.
Y b parameter was fixed as 1 for our model, as the fitted AUC data were normalized to initial value for all OCA. TD 1 and TD 2 parameters were manually fixed as 0 and 11 min, respectively, as the "active" phase starts at t ¼ 0 timepoint and the "passive" phase of our hypothesized biphasic model starts right after all the manipulations are done (t ¼ 11 min). The fitting was performed in Origin 2018 (OriginLab Corporation). To compare simultaneously the amplitude and the time constant of the fitted kinetic AUC data, two ratios were calculated for each dataset. The first one is the slope at origin fitted curve for the first phase (SAO 1 ), defined as SAO 1 ¼ A 1 ∕Tau 1. For the second "passive" exponential, an SAO 2 ratio was defined as SAO 2 ¼ A 2 ∕Tau 2.
Acknowledgements Sergey M. Zaytsev  was supported by  French Embassy in Russia  under the  Vernadski international joint PhD program  of (2018 to 2021), and  Russian Foundation for Basic Research  (Grant No. 20-32-90043 ). Elina A. Genina was supported by the  Russian Foundation for Basic Research  (Grant No. 20-52-56005 ). Valery V. Tuchin was supported by grant under the Decree of the Government of the Russian Federation no. 220 of April 9, 2010 (Grant No. 075-15-2021-615  of June 4, 2021). Marine Amouroux  and  Walter Blondel  acknowledge funding from  CPER IT2MP (Contrat Plan Etat Région « Innovations Technologiques, Modélisation et Médecine Personnalisée ») , the  Ligue Contre le Cancer  and the  FEDER (Fonds Européen de Développement Régional ) of the PhotoVivo platform and from the  French Région Grand-Est , the  Metz-Thionville Regional Hospital (2016 clinical research Award) in the frame of SpectroLive  project.
At enrolment, instructions were given to carers that treatment should be stopped and they should seek advice from the investigator in the event of constipation, urinary retention, pneumonia, allergic reaction, fever, very hot weather or changes in behaviour. After evaluating the event, the investigator will decide if treatment should remain stopped or a lower dose used. The parent/carer has been instructed to complete a notebook daily to record the dosage used and any AEs. Outpatient visits will take place at Day 28 and Day 84. Telephone interviews will occur every week during the titration period and at Day 56. All data are collected by the investigator and their study team at the study site. Pseudonymised data are entered by site personnel into a secure and validated electronic Case Report Form that is managed by the delegated contract research organisation with strict adherence to European Union General Data Protection Regulation.
Identifiable, sensitive information is located securely at the study sites and is made available to delegated members of the study team to verify accuracy of data entry only. After the Day 84 assessment, patients will be invited to continue into a 6-month open-label study extension (OLSE) where all patients will receive glycopyrronium. For those patients who previously received placebo, a period of titration will be performed. Clinic visits are scheduled for Day 168, with a final visit on Day 252.
Acknowledgements The Latin America and the  Caribbean Code Against Cancer  project was co-funded by the  Sociedade Beneficente Israelita Brasileira Albert Einstein (HIAE)  / amigo_h ( Amigos Einstein da Oncologia e Hematologia), Brazil  (Grant number:  DCA-ENV-2020-01 ), and the  International Agency for Research on Cancer (IARC/WHO), France. The systematic reviews have been conducted by the  Iberoamerican Cochrane Centre/ Biomedical Research Institute Sant Pau (IIB Sant Pau)  from Spain. The authors alone are responsible for the views expressed in this manuscript. Where authors are identified as personnel of the  International Agency for Research on Cancer/World Health Organization , the authors alone are responsible for the views expressed in this article and they do not necessarily represent the decisions, policy or views of the  International Agency for Research on Cancer /World Health Organization.
Study Population Sixty adult (18 years old) allo-HSCT recipients who underwent transplantation at the hematology department of the Lyon University Hospital (France) were included in the prospective, single-center cohort study "Vac-cHemInf" between May 2018 and August 2020  [33]  at 6 months after transplantation (interquartile interval [IQR], 5-8 months), before the initiation of the recommended vaccination schedule  [34]. The exclusion criteria applied were post-transplantation relapse of the hematological underlying disease or death  [33]. The cohort was approved by the regional ethics committee (Comit e de Protection des Personnes Sud-Est V, Grenoble, France, number 69HCL17_0769) and is registered in ClinicalTrials. gov (NCT03659773). At inclusion, demographics (age, sex), hematological and transplantationrelated characteristics (underlying disease, conditioning regimen, stem cell source, donor type), as well as post-transplantation characteristics (GvHD, immunosuppressive treatment, post-transplantation infections) were retrieved from medical records through an electronic case report form.
Data treatment and normalization were next performed using nSolver analysis software (version 4. 0, NanoString technologies). Each sample was analyzed in a separate multiplexed reaction, each including 8 negative probes and 6 serial concentrations of positive control probes. Negative control analysis was performed to determine the background for each sample. A first step of normalization using the internal positive controls allowed correction of a potential source of variation associated with the technical platform. To do so, we calculated for all samples the background level as the median +3 standard deviations across the 6 negative probe counts (obtained threshold: 30). Every sample under the background level was fixed to this value. Next, we calculated for each sample the geometric mean of the positive probe counts.
Statistical Analysis Normality testing was performed using the Shapiro-Wilk normality test. Distribution of quantitative data was expressed as mean (range) or median (interquartile range, IQR) where appropriate. For HV and allo-HSCT recipient datasets, analysis of variance was performed using F-tests, and differences were calculated using a parametric unpaired Student's t test with Welch's correction. Gaussian data were analyzed using analysis of variance and nongaussian data using the Kruskal-Wallis test or the Mann-Whitney test, when appropriate. Area under receiver operating characteristic (ROC) curves (AUC [95% confidence interval {95%CI}]) and volcano/whisker plots were plotted using GraphPad Prism software (version 8; GraphPad software, La Jolla, CA). ROC curves were built to identify the most differentially expressed genes between patients with or without viral infectious episodes.
Principal component analysis (PCA) and hierarchical clustering was carried out using Partek Genomics Suite software (version 7. 0; Partek Inc. , St. Louis, MO). The analysis according to the administration of immunosuppressive treatments was conducted using Linear Discriminant Analysis applied on the 5 first component of the PCA to get 80% of the explained variability using "adegenet" R package  [48]. The impact on gene expression of clinical characteristics known to determine speed and quality of immune reconstitution (age, sex, underlying disease, parameters of T-cell reconstitution, parameters of allo-HSCT procedure, GvHD, ongoing immunosuppressive treatment) was analyzed using Wilcoxon test  [3, 49]. Heatmap was generated by scaling and centering log10-transformed gene expressions, and the dendogram was drawn based on hierarchical clustering analysis (Euclidean distance matrix with Ward's method).
Among the 15 biological networks described, 11 were induced both by LPS and SEB stimulation, among which 4 canonical pathways, 3 upstream regulators and 4 diseases and functions (Figure  1D, E ). The other biological networks activated were stimulant-dependent. Complete results of the IPA analysis are available in supplementary data (Supplementary Data S1). Interestingly, the biological networks activated in HV were also activated in allo-HSCT recipients, suggesting that both stimulants induced similar canonical pathways in both populations, driven by common upstream regulators, and leading to the activation of similar diseases and biological functions. Taken together, these results suggest that allo-HSCT recipients are able to activate immune responses similarly to HV in response to stimulation, without taking into account activation levels.
Three genes with a lower expression were common to both SEB and LPS stimulation (CCL23, IFNG, and SPP1). This suggests a potential delay in the regulation of immune response and inflammatory processes in the allo-HSCT population at 6 months after transplantation compared to HV. Of note, the 4 genes whose expression was higher in allo-HSCT recipients were only weakly induced after stimulation and thus not biologically relevant. Complete list of differentially expressed gene between HV and allo-HSCT population are available in supplementary data (Supplementary Data S3). The lower number of DEGs associated with a lower expression level post-stimulation of these DEGs in allo-HSCT recipients led us to compare the enrichment of the biological networks implicated between both populations using an activation score in order to quantitatively evaluate the functional immune response post LPS or SEB challenge.
Transcriptomic IFA Allows Identification of Immune Functional Alteration Unrevealed by Cellular Counts Individual immune responses were observed through PCA performed on 3 datasets; the first 2 were obtained after stimulation with either LPS or SEB, respectively, and the last one was obtained by combining the datasets obtained after LPS and SEB stimulation (cumulative condition). Samples projected onto the first 2 PC revealed an overall variability of 39. 0%, 40. 2%, and 34. 2% in the LPS, SEB, and cumulative conditions, respectively. Interestingly, among the 3 datasets we observed a group of allo-HSCT recipients clustered closely to all HV located on the upper right side of PCA, whereas some patients were distant from HV, suggesting distinct transcriptomic profiles (Figure  3A , Figure  S1A, B ).
Indeed, deciphering immune signatures through transcriptomic analysis might help develop personalized precision medicine. In the context of allo-HSCT, such tests, reflecting the functional immune status, could allow us to better characterize immune reconstitution to optimize prophylactic strategies. There are some limitations to this study. In this pilot study, performed at a single institution, the number of patients included was limited, and the results need to be confirmed. Immune function assessment herein is only representative of the selected stimuli (LPS and SEB). Functional immune reconstitution was studied at a single timepoint, but the ongoing follow-up of the cohort should enable to provide dynamic data regarding the kinetics of immune reconstitution overtime. The biological network assessment might be biased by the custom gene-panel.