dataesr/scientific-paragraphs-categorization huggingface dataset:
The K562-mbIL21 mediated expansion led to an upregulation of CD56 (data not shown) and downregulation of CD16 expression on NK cells compared to baseline. However, only samples from EBV-cHL and HC exhibited a significant decrease in the frequency of CD16 + NK cells (Fig. 3b ). The frequencies of NKG2A + KIR -, NKG2A -KIR + and CD57 + NK cells (Fig. 3c ) did not differ between the three groups. CD62L was not detected on expanded NK cells (data not shown). We performed a degranulation assay with LCL221 coated with rituximab using a lower E:T ratio of 1:2 due to the K562-mbIL21-mediated activation of expanded NK cells. We did observe a decreased frequency of CD107a + NK cells in EBV+ cHL compared to HC close to the statistical significance (mean 36% vs. 49%, one way ANOVA with Tukey's multiple comparisons test P=0.
Nevertheless, the link between the expression of lytic genes and the development of B cell cancers is supported by in vivo studies in mouse models of EBV-associated B cell lymphomas other than cHL  [42] [43] [44]. Novel immunotherapies using bi-specific monoclonal antibodies targeting the CD30 antigen present on HRS cells are currently evaluated as salvage treatment for patients with refractory or relapsed cHL  [4, 20]. Both strategies might rely on the presence of functional NK cells within the TME or in the peripheral blood of cHL patients. The low frequency of blood CD56 dim CD16 + NK cells found in some EBV+ cHL patients might impair the clinical efficacy to the bispecific anti-CD30/CD16A antibody construct AFM13 currently assessed in a phase 2 study of patients with refractory or relapsed cHL (NCT02321592).
Notably, AFM13-based immunotherapy in combination with NK cell-activating cytokines has been shown to expand the quantity of tumorreactive NK cells and to improve NK cell cytotoxicity against tumor cells  [45]. Thus, characterization of the EBV status of HRS cells in newly diagnosed cHL patients might be essential for future immunotherapeutic approaches that may include antibodies targeting CD16 and therefore rely on functional NK cell-mediated ADCC. 1 3  JL and AD. Data collection was performed by TA, EP, MaK and JM. Data analysis was performed by TA, EP, CB, CM, MaK, JM, OC and DG. The first draft and the revised version of the manuscript was written by TA and EP and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Funding Open Access funding provided by Universität Zürich.
Data source, inclusion criteria and study setting BIRDY (Bacterial Infections and antibiotic Resistant Diseases among Young children) is a multi-center cohort study launched to address the lack of epidemiological data concerning drug-resistant neonatal and infantile bacterial infections in three low-income countries (Cambodia, Madagascar and Senegal)  [20]. Nested within the BIRDY study, the present study consecutively recruited pregnant women in Guédiawaye (suburban neighborhood in Dakar) and Sokone (rural area near the Gambian border) primary health centers during their third trimester of pregnancy, from October 2013 to September 2018. In the BIRDY study, healthcare workers interviewed women using a standardized questionnaire and collected from them a vaginal swab for streptococcus B (GBS).
Results A total of 805 pregnant women were included in the Birdy study in Senegal. From October 2013 to September 2018, 477 (62%) women were screened for BV; 457 (96%) had no missing data for variable of interest (center, age, education, gravidity, number of prenatal consultations) and were analyzed, 308 (67. 4%) in the suburban area and 149 (32. 6%) in the rural area. Sociodemographic characteristics, pregnancy followup, immune status and vaginal smear characteristics are described in Table  1.
The choice of guinea pigs for EFD studies is unusual because there is limited background data and precedence for DART evaluations of biologics in this species. Exceptions are the fertility, early development and EFD studies that were performed with natalizumab  7, 8  and fertility studies with guselkumab. 9. Caplacizumab was dosed in the EFD study daily from gestation day (GD) 6 to GD41 at 0, 0. 05, 1 and 20 mg/kg via intramuscular (IM) injection with n = 18-20 pregnant females per group. IM was selected as route of administration as repeated intravenous (IV) dosing is not possible in guinea pig. Laparotomy was performed on GD61 and toxicokinetics (TK) and anti-drug antibodies (ADA) were assessed at 6 time points to confirm exposure throughout the treatment period. The no-observed-adverse-effect level (NOAEL) in this study was established at 20 mg/kg/day without a test item-related increase in the incidence of malformations, variations or retardations.
After discussion of these results with the Paul Ehrlich Institute, a placental transfer study was performed as no fetal drug concentrations were measured in the first study and PD assays (vWF, FVIII and ristocin cofactor activity (RICO)) were not available at the time. Caplacizumab was dosed from GD6 to GD41 or GD61 at 0, 20 and 40 mg/kg/ day via IM injection to 5 animals/group. The NOAEL was at 40 mg/kg/day while active exposure of the dams was demonstrated by the TK data and full suppression of vWF induced platelet aggregation ex vivo in the RICO assay measuring the ability of vWF to interact with the platelet receptor GP1b-IX-V. Low fetal exposure was seen on GD41 and GD61. Active placental transfer of caplacizumab is not expected because high molecular weight proteins (> 5kD) inefficiently cross the placenta by diffusion, as the Nanobody does not interact with the FcRn receptor and as there is no literature evidence for target-mediated placental transfer.
13 Reproductive risk assessment using WoE would comprise a thorough review of public databases and literature, an assessment of the potential effects of target biology, expression, and pharmacology on EFD, a review of existing repeat-dose toxicity data of the respective or related molecule(s) and an assessment of established direct or indirect effects of the therapeutic in nonpregnant animals and patients, as well as on potential placental transfer. The reproductive risk assessment of daratumumab (Darzalex®)  14  was presented in detail as an example of acceptable WoE assessment, based mainly on scientific literature without performing reproductive and developmental toxicology studies. There are several recent FDA approvals of biologics with no EFD studies, as listed by Rocca et al. 15  These authors have also proposed a conceptual decision tree for evaluating whether an in vivo study in pregnant animals is warranted or not.
Challenges include availability of good quality material, sufficient ranking candidates and databases. There is a need for high throughput systems. These strategies provide rather a ranking than a no-go signal across companies; one challenge is to get suitable controls, as they may become less positive than the candidate may. One should also consider that ADAs elicited by many ADA-positive molecules on the market have no effect on PK, PD or safety. Few methods exist to investigate a potential effect of the immune system against self-antigens and primarily focus on the target biology and the safety assessment. Challenges of non-clinical models include the assessment of inducible targets, the animal model itself (young, healthy animals), the limited number of study animals, a potential xenogeneic immune response, species-specific differences or the predictability in a non-disease model.
Session 2: non-animal testing strategies The second session of the meeting was co-chaired by Andrea Kiessling (Novartis) and Petra Schmitt (AbbVie). With animal studies coming increasingly under scrutiny, the evolving field in drug development of non-animal testing strategies is becoming attractive to the safety assessment of biologics. The session's focus was on how such approaches could be applied to biologics. Examples of non-traditional techniques are provided to assess on-and off-target toxicity. Petra Schmitt (AbbVie) asked in her introduction whether the time has come for non-traditional safety assessment approaches of biologics. In the "Replacement, Reduction, and Refinement" (3 R) regulatory age, health authorities have become more willing to consider use of alternative ways of safety assessment, such as translational pharmacological and toxicological databases, complex in vitro models and other non-traditional methods.
7] [28]  The Head of Medical Agencies and the EMA also formed a Joint Big Data Taskforce to discuss key criteria and requirements for large database systems and data use. 29 While chemicals are more commonly the focus of these efforts, reference is made to biologics, and thus the groundwork is laid for a generally applicable principle. In the US, the Critical Path Institute's Predictive Safety Testing Consortium  30  shares internally developed new and improved safety testing methods among 250 participating scientists, which include FDA, EMA and Pharmaceuticals and Medical Devices Agency scientists, with the aim to submit them for regulatory qualification. The six working groups have a strong translational focus and evaluate non-clinical and clinical biomarkers in the areas of kidney, liver, testicular toxicity, pancreatic, skeletal muscle as well as vascular injury.
In drug development, generally the aim of safety evaluation is to gain reliable information that can be used for decisionmaking and that will achieve regulatory acceptance. Not only availability of in vitro and in vivo data, but also a good understanding of the toxic mechanism and the organ or tissue biology increase the credibility of the assessment. In this context, non-animal testing strategies could be useful through a combination of various in vitro techniques, which complement each other, and work in combination with in silico knowledge management and predictive modeling. This way, early on in drug development quantitative structure-activity relationship models, -omics and translational database mining may serve to inform and mature a heuristic computational model and the simplistic understanding of the potential safety profile of the drug.
The second one explored mechanisms of the anti-drug immune response in patientderived samples aiming at increasing the general understand of immunogenicity and identifying markers that may predict an immune repose at a patient level. The third work package addressed the evaluation and development of technologies for predicting immunogenicity. The fourth work package ensured the connection of all data through a database and integration and evaluation of the data to enable identification of potential predictive patterns. In his presentation, Dr. Spindeldreher provided insights into the work performed under work package 3. In the course of ABIRISK, several mouse models were developed and evaluated for utility in the assessment of antigenic potential of biotherapeutics. The so-called BRGSF mouse model  42  was found to be the most promising one giving rise to a KLH-specific human IgM, IgG and CD4 T cell response that was human leukocyte antigen (HLA), and not mouse MHC, restricted (unpublished data).
Focusing on gate 3 (induction of CD4 + T cells response), datasets showing the utility (capacity to generate responses to naïve and recall antigens), sensitivity (capacity to recognize one or more antigenic peptides in a pool of non-antigenic peptides) and specificity (HLA-matched responses) of the MIMIC CD4 + T cell assay were highlighted. As a case study of coupling in silico and biological assays to examine CD4 T cell immunogenicity, published results were shown illustrating how in silico predictions that the newly emergent pandemic A/California/07/2009 (H1N1) virus had cross-reactive T cell epitopes with prior circulating influenza strains could be verified in the MIMIC CD4 T cell assay. 49 Through this analysis, it was shown that the computational tools used were 80-90% accurate in predicting CD4 + T cell epitopes and their HLA-DRB1-dependent response profiles.
The starting dose was selected using the scaled PKPD model to keep the projected cytokine level below a clinically acceptable threshold. IL-6 release was found to determine the starting dose. As the data in healthy cynomolgus monkey do not reflect potential cytokine release from tumor, a 10-fold safety factor was added for selection of the starting dose. This approach allowed EIH at a starting dose markedly higher than the one based on in vitro MABEL assessment. The starting dose was well tolerated. Comparison of projected and observed IL-6 release in patients indicated a tendency of the model to overestimate IL-6 release in patients. Benno Rattel (Amgen) started his presentation by giving the regulatory history behind the generation of the EMA guideline on Strategies to Identify and Mitigate Risks for First-In-Human Clinical Trials with Investigational Medicinal Products issued in 2007 following the TGN1412 incident.
Sequencing and phylogenetic analyses The purified PCR-positive products were sequenced at Macrogen Europe (Amsterdam, the Netherlands). Nucleotide sequences obtained were edited with the MEGA X program  (Kumar et al. 2018 ) and subsequently aligned with the ClustalW program included in MEGA X. Minor corrections, to increase the aligned sequence similarity and improve the inferences on any positional homology, were then made by hand. A BLAST search was carried out in order to elucidate any homologies or similarities with the sequences previously published in the GenBank database. The molecular identification was achieved by phylogenetic analysis through the neighbor-joining distance method  (Saitou and Nei 1987)  with at least 1000 bootstrap replications. Nucleotide sequences obtained.
rat genotype II, Cryptosporidium sp. rat genotype III, and the zoonotic species C. viatorum. Cryptosporidium sp. rat genotype II was the most detected genotype, which has been found widely distributed in North and South of Corsica, while Cryptosporidium sp. rat genotype III was only detected in one rat in the South, as was C. viatorum. A sequence obtained from R. rattus, captured in the South region of Rizzanese (isolate 111C), clustered together with a C. viatorum sequence obtained from Berylmys bowersi from China (GenBank: MK522270). Additionally, six sequences from R. rattus, captured in the North regions of Diane (isolates 7C and 10C), Corte (isolate 89C), and Biguglia (isolate 105C), and in the South regions of Ajaccio (isolate 77C) and Pinarellu (isolate 98C), clustered together with Cryptosporidium sp.
rat genotype II from Rattus tanezumi from Fig. 2  Phylogenetic analysis using the neighbor-joining method with p-distance and 1000 bootstrap replications based on the 18S ribosomal RNA fragment (830 bp). Toxoplasma gondii was used as the outgroup China (GenBank: GQ121025). One sequence from R. rattus, captured in the South region of Rizzanese (isolate 113C), clustered together with Cryptosporidium sp. rat genotype III from R. rattus from Australia (GenBank: JX294371). Four different haplotypes of Cryptosporidium sp. rat genotype II were detected. The most prevalent, haplotype 1, was found in the North (isolates 10C and 89C) and South (isolate 77C) of Corsica. One second haplotype, haplotype 2, was identified in the North of the island (isolate 105C), whose nucleotide sequence presented two extra adenine bases in positions 172 and 173 with respect to haplotype 1.
[1]. In addition, we investigate the role of auto-Abs against IFN-I that were not neutralizing in vitro in COVID-19 and non-COVID-19 patients. From 16 March 2020 to 8 June, 2020, 210 of 473 (43%) SARS-CoV-2 positive patients diagnosed by specific real time polymerase chain reaction (RT-PCR), hospitalized in Amsterdam UMC were selected based on sample availability and evaluated. As controls, 37 intensive care unit (ICU) patients were included who were admitted for other reasons than respiratory viral illness including COVID-19 (Supplemental Information). Clinical data were collected prospectively and blood samples were tested for auto-Abs against IFN-α2 and IFN-ω with a multiplex particle-based assay and ELISA. Blocking activity of auto-Abs was measured with a STAT1 phosphorylation assay. Auto-Abs against IFN-I were found in 35 of 210 (17%) COVID-19 patients of whom 6 of 35 (17%) had neutralizing auto-Abs (Table  1 ).
SARS-CoV-2 Transmission and Mortality The data for SARS-CoV-2 transmission and mortality came from the coronavirus page of "Our World in Data" database (github. com/owid/covid-19-data/tree/master/public/data). The cumulative number of COVID-19 reported cases in the African countries from the beginning of the pandemic on February 14, 2020, to February 4, 2021, were extracted. Given that we.
Independent Variables The covariates used in the analysis were collected across several fields of science, including clinical/diseases data, health system performance, demographic parameters, economic indicators, climatic variables, the levels of pollution and radiation, and the use of social media. The data were collected from publicly available and well-known, reliable sources, such as the World Bank Open Data, the Health Nutrition and Population Statistics, the WHO Global Health Observatory (GHO), the Institute of Health Metrics and Evaluation (IHME), the Global Health Security Index (GHS), and the Our World in Data databases among the others. The description of each covariate by category is reported in Table  1. These variables were included based on the list of variables previously hypothesized to be among the possible reasons for the low burden of COVID-19 in Africa.
worldbank. org/indicator/EN. URB. MCTY. TL. ZS million, % of total population Clinical or diseases Cardiovascular Death rate due to cardiovascular diseases, per 100,000 github. com/owid/covid-19-data/tree/master/public/data diseases population Diabetes Diabetes prevalence (% of population ages 20 to 79) data. worldbank. org/indicator/SH. STA. DIAB. ZS TB Incidence rate of TB, per 100,000 people data. worldbank. org/indicator/SH. TBS. INCD BCG BCG vaccination coverage, in % databank. worldbank. org/source/health-nutrition-and-population-statistics LRI Lower respiratory infections rate, per 100 000 population ghdx. healthdata. org/gbd-results-tool HIV Prevalence of HIV, total (% of population ages 15-49) databank. worldbank. org/source/health-nutrition-and-population-statistics Malaria Reported cases of malaria, absolute number databank.
org/grapher/share-of-population-with-cancer?tab=table Epidemiological Transmission WHO transmission classification (community covid19. who. int/table level transmission versus no community transmission Health system related Testing capacity Total tests for COVID-19 per one million population population. un. org/wpp/Download/Standard/Population/ Nurses Number of nurses and midwives per 1000 population data. worldbank. org/indicator/SH. MED. NUMW. P3 Physician Number of physicians per 1000 population data. worldbank. org/indicator/SH. MED. PHYS. ZS GHS index Global health security detection index (Weighted sum of https://www. ghsindex. org/ all the GHS data normalized to a scale of 0 to 100, where 100 = best health security condition) Socio- economic GDP Gross domestic product per capita github.
com/owid/covid-19-data/tree/master/public/data GINI Index GINI index (income inequality, 100 = high github. com/owid/covid-19-data/tree/master/public/data Doing business Ease of doing business rank 2019 (1=most databank. worldbank. org/source/doing-business#selectedDimension_Country business-friendly regulations) Human Human development index ourworldindata. org/human-development-index development index ClimaticTemperatureTemperature in degree Celsius, annual mean https://tinyurl. com/d3pnyu9mRainfallRainfall in mm, annual mean tinyurl. com/d3pnyu9m(Continued).
Acknowledgments: Funding: BV and SL were supported by postdoctoral fellowship grants of the  Research Foundation --Flanders  (Fonds voor  Wetenschappelijk Onderzoek ,  12U7121N  and  12R4718N , respectively). MTPG acknowledges  Danish National Research Foundation Award   DNRF143  for funding. This work was funded in part by the  Intramural Research Program  of the  National Institute of Allergy and Infectious Diseases of the NIH (DMM and JKT). MAS was supported by  US National Institutes of Health  grants  HG006139  and  AI135995. MW was supported by the  Bill and Melinda Gates Foundation  ( INV-004212 ) and the  David and Lucile Packard Foundation. The research leading to these results has received funding from the  European Research Council  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement no.
Patient Cohort SAVIMMMUNE RETRO was designed as a retrospective multicenter observation study. We invited 12 French center investigators that were willing to participate in this survey to collect data from all consecutive advanced (Stage IIIC/IV) NSCLC patients, PS 3 or 4, who received at least one course of salvage nivolumab, within a 24-month time frame ranging from April 2015 to April 2017. Clinical records were extracted and collected locally by investigators, with survival data updated on 16 August 2020. PS at diagnosis and upon treatment was assessed from the patients' charts, based on the ECOG criteria, given that the selected investigators were deemed familiar with selecting patients for academic (French Intergroup, IFCT) or company-sponsored clinical trials, which require such a systematic evaluation.
The de-identified database was stored in the electronic system of the Early Phase Unit INSERM CIC-1425, University Hospital Bichat, Assistance Publique-Hôpitaux de Paris, in compliance with the French regulatory rules to preserve the privacy of patient's safety, such data electronic files were declared to the "Comité National Informatique et Liberté" (CNIL). A written informed consent was obtained from all patients that were still alive at the time of data recording, whereas an information sheet stating the right to modify or oppose data collection was provided by the investigators to each deceased patient's relatives. The study protocol was approved by the Institutional Review Board of the French Society of Respiratory Diseases (SPLF) and was registered under the CEPRO number #2020-061-R1.
Variables whose value was known prior to the nivolumab treatment (thus excluding response to nivolumab, TRAEs, and PS change from baseline) were included in the final model, according to their statistical significance in the univariate analysis (cutoff, p = 0. 10). Statistical analyses were performed using IBM SPSS Statistics for Windows, Version 25. 0 (IBM Corp. , Armonk, NY, USA) and GraphPad Prism Version 8. 4. 3 for Windows (GraphPad Software, San Diego, CA, USA). Graphics were rendered using the latter tool, in addition to Microsoft Excel Version 2013 for Windows (Microsoft Corporation, 2013), and RStudio Version 1. 3. 1093 (Integrated Development for R. 2009-2020 RStudio, PBC, Boston, MA, USA).
One should be aware that our results should not be overinterpreted and lead to over treatment in advanced poor PS NSCLC patients, with all the inherent consequences, mainly adverse events and economic burden for patients and families, taking into account the scarce available prospective data in such patients. Second, the major factors conditioning ECOG PS 3-4, such as precise disease burden or comorbidities, were not assessed in the current study. Third, only PD-L1 expression was employed as a predictive marker for anti-PD-1 therapy response and survival; therefore, we were not able to study TMB, which is not routinely measured in France. TMB could possibly explain the influence of smoking in our multivariate analysis, since heavy smokers are usually thought to exhibit higher TMB.