dataesr/scientific-paragraphs-categorization huggingface dataset:
Oral exposure to BPs exacerbates humoral response Oral tolerance is manifested by the absence of humoral response (anti-OVA IgG) to the orally administered antigen (OVA) corresponding to the OVA-tolerized mice. In control mice (i. e. , not exposed to BP, Vehicle (Veh) group, OVA tolerization (i. e. , OVA oral ingestion prior to OVA systemic immunization) significantly decreased serum anti-OVA IgG titers in comparison to immunized mice, validating our protocol for induction of oral tolerance to OVA (Figure  1 A, B  and C ). Anti-OVA IgG titers in OVA-tolerized mice remained significantly higher in BPA5 (Figure  1A ), BPS0. 5 and BPS5 (Figure  1B ), and BPF0. 5 and BPF5 (Figure  1C ) compared to OVA-tolerized veh group, demonstrating a non-monotonic dose-response relationship. The loss of the significant difference between OVA-tolerized group and corresponding OVAimmunized group suggested a defect of oral tolerance to food antigens.
As expected, in vehicle (Veh) mice after OVA-immunization, in vitro OVA restimulation induced an increase of IFN-γ concentration in spleen cell culture supernatants compared to OVA-tolerized group (Figure  2A ). In BPA-treated mice at low doses of 0. 5 in tolerized group and 5 µg/kg bw/d in tolerized and immunized group, in vitro OVA restimulation induced a sharp increase of IFN-γ release by splenocytes compared to the vehicle group (Figure  2A ). In a same way, after BPS and BPF exposure to low dose (0. 5 µg/kg bw/d), an increase of IFN-γ level in splenocyte supernatant from tolerized mice was measured (Figure  2B  and C ). Furthermore, in OVA-immunized mice, splenocytes isolated from BPA5, BPS50 and BPF0. 5-exposed animals displayed higher IFN-γ production following in vitro OVA restimulation compared to non-exposed controls (Figure  2A, B  and C ).
Investigating IL-17 secretion in our model, we noticed that immunization was not associated with increased IL-17 secretion after OVA in vitro restimulation, suggesting that immune response toward antigen in immunized mice is not dependent on IL-17. Interestingly, whereas BPA induced no difference in the production of IL-17 in tolerized or immunized animals, low doses of BPS and BPF (0. 5 µg/kg bw/d) led to a significant increase of IL-17 production after in vitro OVA restimulation in OVA-tolerized mice (Figure  2E  and Figure  2F ). In addition, the functionality of T lymphocytes was investigated by in vitro anti-CD3/CD28 restimulation. Because there was no consequence of tolerization and immunization protocol on the TcR-dependent cytokine response (supplementary data figure  1 ), the two groups of tolerized and immunized mice were pooled.
Without stimulation, BPs did not modify either IFN-γ or IL-17 secretion by splenocytes (data not shown). After anti-CD3/CD28 antibody in vitro restimulation, higher level of IFN-γ was induced in BPA0. 5 and BPS0. 5 groups in comparison with vehicle (Figure  3A  and B ). In BPF-exposed mice, a significant increase of IFN-γ level was also observed, regardless of the dose of bisphenol (Figure  3C ). Concerning IL-17 production, the level of this cytokine was also higher in splenocyte supernatants from BPA0. 5, BPA5, BPF0. 5 and BPF5 groups compared to vehicle group (Figure  3 D  and F ). In summary, bisphenols (A, F and S) not only altered splenic T cell specific immune response but also non-specific response at low dose.
Study population As previously described in detail for this "REMI" (relation between aldosterone and cardiac REmodeling after Myocardial Infarction) cohort  [1] , patients successfully treated by primary percutaneous transluminal coronary angioplasty for a first MI and with an initial occlusion or sub-occlusion of the MI-related coronary artery at angiography, were prospectively included. Main exclusion criteria were: any other significant cardiac disease, any contraindication to MRI, absence of sinus cardiac rhythm, a multivessel disease at coronary angiography, and a >12h delay-time between the onset of chest pain and reperfusion. All subjects gave signed informed consent to participate. The study protocol complied with the principles of the Declaration of Helsinki, was approved by the local Ethics Committee (Comitede Protection des Personnes EST-III, agreement n˚2009-A00537-50) and registered on the ClinicalTrials.
Cardiovascular MRI As detailed previously  [1, 3, 4] , MRI exams were performed on a single 3. 0 Tesla magnet (Signal HDxt, GE Healthcare, Milwaukee, Wisconsin) with a cardiac coil. Systolic, diastolic and mean brachial blood BP were measured with an automated sphygmomanometer (Maglife C, Schiller Medical, Wissembourg, France). Three measurements were obtained during each MRI examination and mean values were stored for analyses herein. A steady-state free precession pulse sequence was used to assess cardiac function in contiguous short axis planes, as previously detailed  [3, 4] , and LV end-diastolic volume, end-systolic volume, LV mass and ejection fraction were obtained using dedicated software (MASS research v2013-exp™, Medis, Leiden University Medical Center, The Netherlands). The LV concentric remodeling index was computed as the LV mass over end-diastolic volume ratio  [1, 3, 4].
Statistical analyses All analyses were performed using the SAS software version 9. 4 (SAS Institute Inc. , Cary, NC, USA). The two-tailed significance level was set at P <0. 05. Continuous variables are expressed as median with interquartile range (Q1 -Q3) and categorical variables as frequencies (percentages). Comparisons of characteristics between SVR groups were carried out using non parametric Kruskall-Wallis tests for continuous variables and Fisher's exact tests for categorical variables. Associations between the baseline characteristics and the SVR status were additionally assessed using univariable and multivariable ordinal logistic regression models with baseline characteristics as explanatory variables and the three SVR categories as outcome variable, namely SVR-(as reference category), SVR+ and SVR++.
We have recently identified an 11-SNP haplotype (a1), which adds further specificity to the description of this particular genetic association  [23, 24]. This SNP-haplotype spans 0. 25 megabases (mb) of DNA surrounding the HLA-DRB1 gene on the short arm of chromosome 6. It has the most significant association with MS of any SNP-haplotype in the genome, and it is tightly linked to the HLA-DRB1*15:01~HLA-DQB1*06:02 haplotype  [23, 24]. For example, 99% of these (a1) SNP-haplotypes carry this HLA-haplotype and, conversely, 99% of these HLA-haplotypes carry the (a1) SNP-haplotype. In the Welcome Trust Case Control Consortium (WTCCC) dataset, the odds ratio (OR) for an association the full HLA-DRB1*15:01~HLA-DQB1*06:02~a1 haplotype with MS was 3. 28 (p<<10 -300 ) and similar disease associations for portions of this haplotype have been consistently reported in many other MS populations across Europe and North America  [11, [15] [16] [17] [18] [19] [20] [21] [22].
7/23) of (H+)-carriers are even genetically susceptible to MS. Indeed, taken together, the fact that only half of MS-patients are in the (H+) subset, and the fact that this estimate for genetic susceptibility represents an upper bound, indicates that the actual percentage of (H+) carriers who are genetically susceptible must be far less than this 20% figure. Nevertheless, essentially all of the conserved extended haplotypes (CEHs) that carry (H+) -even those unique in the WTCCC dataset -are associated with MS  [58]. Therefore, it is likely that the (H+)-haplotype, itself, contributes to genetic susceptibility. Despite this contribution, however, the majority of (H+)-subset members have no chance whatsoever of developing MS. Therefore, at least with respect to the (H+) haplotype, genetic susceptibility to MS must result from the combined effect of (H+) together with the effects of certain other (as yet, unidentified) genetic factors (see Supplemental Material).
Statistical Analysis Assay data were expressed as the mean ± standard error of the mean (SEM) per 10 6 epithelial cells, mean ± SEM per 100 mg lung tissue or mean ± SEM per ml, as appropriate. The relative expression of mRNAs was calculated according to the 2 (-DCt)  method  (Livak and Schmittgen, 2001). In all the series of experiments, "n" corresponds to the number of donors from whom hBECs were isolated. The statistical analyses involved a one-way repeated-measures analysis of variance (Friedman's test), followed by Bonferroni's or Dunn's post-tests or t-tests, as appropriate. All analyses were performed using GraphPad Prism ® software (version 5. 01, GraphPad Software Inc. , San Diego, CA, USA). The threshold for statistical significance was set to p < 0. 05.
The ASDAS combines all these elements in a weighted equation resulting in a continuous score with better validity, discriminative capacity and improved ability to detect changes compared with the individual variables and the BASDAI  [4, 5]. Furthermore, ASDAS allows the definition of four validated disease activity states (inactive disease, low, high and very high disease activity) and two response measures (major and clinically important improvement, MI and CII)  [6, 7]. Despite the very good performances of ASDAS  [4, 5, 8]  there are still situations in which ASDAS cannot be calculated: in fact, databases built before 2009 might not collect all necessary variables, especially PGA, considering it is not part of the BASDAI. As a partial solution to this problem, a previous work proposed a surrogate ASDAS formula [BASDAI-based ASDAS (BASDAS)] that could be calculated only from BASDAI total and CRP, ignoring the individual questions of the BASDAI and the PGA included in the original ASDAS  [9].
Study population Longitudinal data from six different axSpA cohorts [four observational cohorts and two randomized controlled trials (RCTs)] were used, in order to ensure a wide variety of disease severity, duration and activity. Patients with nr-axSpA and r-axSpA, with and without an active intervention, were included from the following cohorts: i. SPondyloArthritis Caught Early (SPACE)  [12] , an ongoing multicentre European cohort enrolling patients with chronic back pain (!3 months, 2 years, onset <45 years) of unknown origin. For this study, we included patients with a diagnosis of axSpA by a rheumatologist, with a level of confidence !7 (0-10 scale) and fulfilling ASAS classification criteria  [1]. ii. 'Devenir des spondyloarthropathies indifferenciees recentes' (DESIR)  [13] , a multicentre French cohort of patients with early inflammatory back pain (<3 years' duration) suggestive of axSpA.
For this study, axSpA patients meeting the AxSpA ASAS classification criteria were used  [1]. iii. Outcome in Ankylosing Spondylitis International Study (OASIS)  [14] , a three-country European cohort that included consecutive patients with r-axSpA. iv. Norwegian Disease-Modifying Antirheumatic Drug study (NOR-DMARD)  [15] , a registry including patients with a diagnosis of r-axSpA (according to a rheumatologist) and starting a new DMARD regimen from December 2000 onwards. We considered data from the period 2000-09. v. Ankylosing Spondylitis Study for the Evaluation of Recombinant Infliximab Therapy (ASSERT)  [16] , a multicentre phase 3 RCT in patients with r-axSpA treated with infliximab vs placebo.
Development and validation vi. RAPID-axSpA  [17] , a multicentre phase 3 RCT in patients with r-axSpA and nr-axSpA treated with certolizumab pegol vs placebo. Patients included in SPACE, DESIR and OASIS were treated according to best clinical practice and evaluated at fixed time points, not linked to starting interventions. In the merged cohort, the following time points were considered for homogeneity: baseline, 12 weeks (if available, e. g. NOR-DMARD and RAPID-axSpA), 6 months, and 1, 2, 3 and 4 years after inclusion. For the present analysis, we only included patients with complete data on all BASDAI questions, CRP, PGA and ASDAS.
Ethics Available data from longitudinal cohorts was used. Approval of local Ethical Committee was granted for all centres participating to the longitudinal cohorts or RCT. All studies were conducted in accordance with applicable regulatory and International Conference on Harmonization Good Clinical Practice requirements, based on the Declaration of Helsinki and local laws. All patients provided written informed consent prior to any protocol-specific procedures. Three of the cohorts were registered as trials and ClinicalTrials. gov identifiers were as follows: NOR-DMARD: NCT01581294; ASSERT: NCT00207701; RAPID-axSpA: NCT01087762.
Alternative ASDAS formulae The selection process for potential PGA substitutes was conducted filtering candidate replacements according to the following criteria: (i) substitutes needed to be patient-reported outcomes, like PGA, to ensure face validity; (ii) wide availability in databases; and (iii) correlation between the PGA substitute and PGA >0. 6 in the pooled cohort. This led to the exclusion of PhGA and night pain, as these are not frequently available in databases. Therefore, the possibilities were narrowed to questions from the BASDAI, individually or combined. The PGA substitutes derived from BASDAI that fulfilled the above-explained criteria and that we hereby present are: fatigue (Q1); back pain (Q2); average of fatigue and enthesitis (Q14); average of back pain and peripheral joint pain/swelling (Q23); average of back pain and duration of morning stiffness (Q26); average of fatigue, enthesitis and morning stiffness severity (Q145); average of back pain, peripheral joint pain/swelling and duration of morning stiffness (Q236); and BASDAI total score (BT).
Statistical analysis In the pooled cohort, Pearson's correlations between PGA substitutes and PGA were analysed: a correlation coefficient >0. 6 was defined a priori as good. For the constant option, the correlation between ASDASwithout-PGA and PGA was calculated. Subsequently, the merged database was randomly split in a development and a validation cohort (1:1). The development cohort was used to define conversion factors, i. e. the values to be applied in ASDAS formula when multiplying the PGA substitute. GEE models, with multiple visits per patient, having PGA as dependent variable and a PGA substitute as main independent variable were built. Models were adjusted for sex and disease duration and the beta coefficient was used as the conversion factor. Since correlations between PGA values across time points did not display a regular pattern, the 'unstructured' correlation matrix was selected.
e. sensitivity to change) was examined in a sub-population pertaining to NOR-DMARD, ASSERT and RAPID-axSpA, where data were available at start of treatment and follow-up. Different analyses were used: (i) kappa between original-ASDAS and alternative indices in MI/CII achievement at 6 months; (ii) v 2 tests comparing the percentage of patients reaching MI/CII in the treatment vs placebo arm of the RCTs at 6 months, according to each ASDAS formula (higher v 2 ¼ better discrimination); (iii) comparison by ANOVA of mean ASDASchange scores at 12 weeks by change-categories defined through global rating of change (higher F-test ¼ higher discrimination); and (iv) SMD comparing mean ASDASchange at 6 months in the treatment and placebo arm of the RCTs for each formula (higher SMD absolute value ¼ better discrimination).
Development cohort The beta coefficients of the PGA substitutes in the GEE models, with their 95% CIs, were: Q1 ¼ 0. 67 (0. 65, 0. 70); Q2 ¼ 0. 80 (0. 78, 0. 82); Q14 ¼ 0. 84 (0. 81, 0. 86); Q23 ¼ 0. 88 (0. 86, 0. 91); Q26 ¼ 0. 85 (0. 83, 0. 87); Q145 ¼ 0. 90 (0. 88, 0. 93); Q236 ¼ 0. 94 (0. 91, 0. 96); BT ¼ 0. 99 (0. 96, 1. 01); and ASDAS-without-PGA ¼ 2. 14 (2. 06, 2. 21). These were used as conversion factor to replace PGA in the ASDAS formula (Table  1 ). Validity aspects of the alternative ASDAS formulae were assessed both in the development cohort and in the validation cohort. As the latter is the actual validation, these data are presented in detail. The results of the analyses in the development cohort are very similar to the data of the validation cohort (supplementary Tables S2-6, Fig.
Development and validation Discrimination between ASDAS-change scores at 12 weeks by global rating of change categories was overall good, with ASDAS-C3 displaying the highest Ftest (Table  5 ). SMD between ASDAS-change scores in treatment and placebo arm at 6 months were higher for ASDAS-Q2, -Q26 and -Q145, with mean values closer to those of original-ASDAS for ASDAS-Q2, -Q26 and -BT. Still, the discriminatory capacity of the other alternative formulae was also very good (Table  5 ). Feasibility ASDAS-BT was considered highly feasible, as BT has been the most frequently measure of disease activity and is often already available in databases  [24]. A further favourable aspect was the conversion factor % 1. 00 (conversion factor for BT ¼ 0. 99; within the ASDAS-BT formula shown in Table  1 : 0.
Discussion The present study showed that, when PGA is missing, the alternative-ASDAS using the BASDAI total as replacement is the most truthful, discriminative and feasible index. Some other indices performed well in the aspects of truth and discrimination, but this alternative-ASDAS was also the most feasible option. In fact, BASDAI total is usually readily available, without additional calculations required. Moreover, the conversion factor was % 1. 00; thus, in practice, the same multiplying factor of the original formula can be used. Candidate substitutions were sought among patientreported outcomes, in particular within BASDAI, as probably no other disease activity measure is so widely available in axSpA cohorts  [25]. We considered other possibilities, such as PhGA or night pain; however, these variables are frequently unavailable in existing databases, so are not a true solution for an alternative ASDAS when PGA is not available.
For this reason, it can be considered an improved formula compared with BASDAI and/or CRP alone, addressing at least the criticisms to BASDAI about the equal weight of First two columns: agreement (kappa statistics) of each alternative ASDAS with the original-ASDAS in the percentages of patients reaching ASDAS-defined major improvement and clinically important improvement criteria at 6 months. Third and fourth column: percentage of patients reaching ASDAS major improvement or minimal clinically important improvement at 6 months in treatment vs placebo arm in the randomized controlled trials (ASSERT and RAPID-axSpA). a For this analysis only patients from NOR-DMARD, ASSERT and RAPID-axSpA database were considered, as they started a new therapy at baseline according to protocol. b For this analysis only patients from ASSERT and RAPID-axSpA were considered, as both treatment and placebo arm were available.
A limitation of the present study is that, for some of the analyses, only part of the study population could be used (e. g. for sensitivity to change, only populations from the RCTs and NOR-DMARD were used). However, there is no consensus about sample size determination for validation studies. A rule of thumb of at least 50 participants has been suggested, although much larger samples might be needed to ensure precise estimates  [28]. The important strengths of the study are: (i) the use of a fairly large and heterogeneous population, which increases generalizability and (ii) the fact that validation analyses have been performed twice, once in the development and once in the validation cohort, with comparable findings, enhancing the robustness of our results. In conclusion, when PGA is not available in existing databases, BASDAI-total can be used as a replacement to calculate alternative-ASDAS for research purposes, essentially without the need for a conversion factor.
This results in an alternative formula [0. 12 Â Q2 þ 0. 06 Â Q6 þ 0. 11 3 1 3 BASDAI-total þ 0. 07 Â Q3 þ 0. 58 Â Ln(CRP þ 1)] resembling the original-ASDAS closest. We strongly recommend the use of ASDAS in its original version whenever possible, but calculation of alternative-ASDAS is now possible even in those cohorts where PGA was not initially collected, enabling new analyses from existing cohorts without PGA. First column: ANOVA between ASDAS change scores at 12 weeks in different change statuses as defined by an external anchor (global rating of change). Second column: SMD in ASDAS change scores at 6 months in treatment vs placebo arm in the randomized controlled trials. a For this analysis only patients from NOR-DMARD, RAPID-axSpA database were considered, as they started a new therapy at baseline according to protocol and had an evaluation available at 12 weeks.
b For this analysis only patients from ASSERT and RAPID-axSpA were considered, as both treatment and placebo arm were available; evaluation was performed at 6 months as available in both databases. The alternative-ASDAS formulae were calculated using, as a replacement for pGA: question 1 (Q1), Q2, average of Q1 and Q4 (Q14) or Q2 and Q3 (Q23) or Q2 and Q6 (Q26), average of questions Q1, Q4 and Q5 (Q145) or Q2, Q3 and Q6 (Q236) and total score (BT) of the BASDAI, or a constant (-C3). ASDAS: Ankylosing Spondylitis Disease Activity Scores; original-ASDAS: ASDAS according to the usual formula  [5, 6] ; ANOVA: analysis of variance; SMD: standardized mean differences; NOR-DMARD: Norwegian Disease-Modifying Antirheumatic Drug study; ASSERT: Ankylosing Spondylitis Study for the Evaluation of Recombinant Infliximab Therapy.
Acknowledgements We would like to thank all patients that participated in all the included studies, as well as the Rheumatology Departments and physicians who contributed to the data collection. A. O. was supported by an  ASAS  fellowship for this project. P. M. is supported by the  National Institute for Health Research (NIHR) University College London Hospitals (UCLH) Biomedical Research Centre (BRC). The views expressed are those of the author and not necessarily those of the (UK) National Health Service (NHS), the  NIHR  or the (UK) Department of Funding: No specific funding was received from any bodies in the public, commercial or not-for-profit sectors to carry out the work described in this article. Individual funding was received by some authors and it is detailed in the Acknowledgements section.
Database To evaluate the effect of this booklet on prescriptions, we retrospectively compared the daily number of times that a drug was administered to each patient between nursing homes of the intervention and control groups using a difference-in-differences design. The data were derived from a database containing information about prescriptions in 519 French nursing homes that were using an electronic pill dispensing system manufactured and marketed by Medissimo (Poissy, France) at the time of the study. This system has previously been described  [9]. Briefly, the pill dispenser contains 28 days of treatment, with as many compartments as drugs and times of administration. It is prepared by qualified staff in the pharmacy. During the preparation, information about the individuals (age and sex) and their prescriptions (drugs, dose, time of administration, duration of prescription, and price of each drug) is entered into specific software to ensure traceability, and accurately describes drug consumption  [34].
Main outcome We compared the average daily number of times a drug dosage unit was administered for each nursing home resident between 1 January 2011 and 31 December 2014 in 113 nursing homes of the Korian group who introduced the booklet (all private, for-profit establishments), versus 406 nursing homes who did not introduce the booklet (private for-profit, private not-for-profit, and public establishments). We chose the number of daily drug dosage units (e. g. , a drug from a single therapeutic class administered morning, noon and evening would count as 3 dosage units) as the main outcome, first, because it was reliably recorded in our database, and second, because drug dispensing has been shown to be a suitable proxy for drug consumption  [35, 36]. No specific definition of polypharmacy was applied in our study, since polypharmacy (as a binary variable, i.